Advanced Driver Fatigue Research 

April 2007 
Foreword 

This report summarizes the findings of the Advanced Driver Fatigue Research project 

conducted by the Center for Intelligent Systems Research (CISR) of the George 

Washington University (GWU), and funded by the Federal Motor Carrier Safety 

Administration (FMCSA) of the U.S. Department of Transportation (USDOT). The goal 

of this project was to develop an unobtrusive drowsy driver detection system for 

commercial motor vehicles. CISR previously developed an effective drowsiness 

detection system for automobiles that relied solely on the driver’s steering patterns for 

input. This project’s evaluation of this system confirms that this approach is equally 

valid for truck drivers, despite the differences between truck and automobile driving. 

The drowsy driver detection system exhibited acceptable false positive and false negative 

readings and an ideal warning rate before crashes. 

Although the report can be helpful to the general public in understanding fatigue in 

commercial trucking operations, the report is primarily targeted toward commercial 

motor carriers and drivers. 

This publication is considered a final report and does not supersede another publication. 

Notice 

This document is disseminated under the sponsorship of the Department of 

Transportation in the interest of information exchange. The United States Government 

assumes no liability for its contents or use thereof. 

This report does not constitute a standard, specification, or regulation. 

The United States Government does not endorse products or manufacturers. Trade or 

manufacturers’ names appear herein only because they are considered essential to the 

object of this document. 
Technical Report Documentation Page (Form 1700.7)

1. Report No. 

FMCSA-RRR-07-001 

2. Government Accession No. 3. Recipient’s Catalog No. 

5. Report Date 

April 2007 

4. Title and Subtitle 

Advanced Driver Fatigue Research 

6. Performing Organization Code 

7. Authors 

Azim Eskandarian, Riaz Sayed, Pierre Delaigue, Jeremy Blum, and Ali Mortazavi 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Center for Intelligent Systems Research (CISR) 

School of Engineering and Applied Science 

The George Washington University 

20101 Academic Way 

Ashburn, VA 20147 

11. Contract or Grant No. 

DTMC75-03-H-00001 

13. Type of Report and Period Covered 

Final Report, September 2003-December 2005 

12. Sponsoring Agency Name and Address 

U.S. Department of Transportation 

Federal Motor Carrier Safety Administration, Office of Research & Analysis 

400 Virginia Ave., SW, Suite 600 

Washington, DC 20024 

14. Sponsoring Agency Code 

15. Supplementary Notes 

Albert Alvarez was the FMCSA contracting officer technical representative for this project. 

 16. Abstract 

This report summarizes the findings of the Advanced Driver Fatigue Research project conducted by the Center for Intelligent Systems 

Research (CISR) of the George Washington University (GWU), and funded by the Federal Motor Carrier Safety Administration (FMCSA) 

of the U.S. Department of Transportation (USDOT). The goal of this project was to develop an unobtrusive drowsy driver detection 

system for commercial motor vehicles. CISR previously developed an effective drowsiness detection system for automobiles that relied 

solely on the driver’s steering patterns for input. This project’s evaluation of this system confirms that this approach is equally valid for 

truck drivers, despite the differences between truck and automobile driving. The drowsy driver detection system exhibited expected false 

positive and false negative readings and an ideal warning rate before crashes. 

17. Key Words 

Collision Avoidance, Commercial Motor Vehicle, Driver, Driving, 

Drowsy, Fatigue, PERCLOS, Sleep, Steering, Truck 

18. Distribution Statement 

19. Security Classif. 

(of this report) 

Unclassified 

20. Security Classif. (of 

this page) 

Unclassified 

21. No. of Pages 

210 

22. Price 
SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FRO

Symbol When You Know Multiply By To Find Symbol Symbol When You Know Multiply By 

LENGTH LENGTH 

in inches 25.4 millimeters mm mm millimeters 0.039 incft feet 0.305 meters m m meters 3.28 feeyd yards 0.914 meters m m meters 1.09 Yami miles 1.61 kilometers km km kilometers 0.621 mi

AREA AREA 

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 squft2 square feet 0.093 square meters m2 m2 square meters 10.764 squyd2 square yards 0.836 square meters m2 m2 square meters 1.195 squac acres 0.405 hectares ha ha hectares 2.47 acrmi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 squ

VOLUME VOLUME 

fl oz fluid ounces 29.57 milliliters ml ml milliliters 0.034 flugal gallons 3.785 liters l l liters 0.264 gaft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 cuyd3 cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 cu

MASS MASS 

oz ounces 28.35 grams g g grams 0.035 oulb pounds 0.454 kilograms kg kg kilograms 2.202 poT short tons (2000 lbs) 0.907 megagrams Mg Mg megagrams 1.103 sho

TEMPERATURE (exact) TEMPERATURE (exac

°F Fahrenheit 5(F-32)/9 Celsius °C °C Celsius 1.8 C + 32 

temperature or (F-32)/1.8 temperature temperature 

ILLUMINATION ILLUMINATION 

fc foot-candles 10.76 lux lx lx lux 0.0929 fl foot-Lamberts 3.426 candela/m2 cd/m2 cd/m2 candela/m2 0.2919 

FORCE and PRESSURE or STRESS FORCE and PRESSURE or S

lbf pound-force 4.45 newtons N N newtons 0.225 psi pound-force per square inch 6.89 kilopascals kPa kPa kilopascals 0.145 

* SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380.
TABLE OF CONTENTS 

 

EXECUTIVE SUMMARY..................................................................................................................1

1. INTRODUCTION........................................................................................ 2 

2. LITERATURE REVIEW............................................................................... 5 

PROBLEM BACKGROUND..............................................................................................................5

FACTORS CONTRIBUTING TO FATIGUE RELATED ACCIDENTS......................................6

Sleep Deprivation............................................................................................................................................7

Work Schedule ................................................................................................................................................7

Sleep Disorder/Quality of Sleep......................................................................................................................8

Time of Day ....................................................................................................................................................8

Monotony of Road/Driving Conditions...........................................................................................................8

Driver Personality and Age .............................................................................................................................9

CHARACTERISTICS OF DROWSINESS/FATIGUE RELATED ACCIDENTS .......................9

EARLY ATTEMPTS AT DETECTING DRIVER DROWSINESS .............................................10

PREVIOUS STUDIES .......................................................................................................................11

SENSING DRIVER’S PHYSICAL AND PHYSIOLOGICAL PHENOMENON .......................11

Eyelid Closure...............................................................................................................................................12

Electroencephalogram...................................................................................................................................13

SUITABILITY OF VEHICLE PERFORMANCE OUTPUT FOR DROWSINESS 

DETECTION......................................................................................................................................14

Vehicle Steering Activity ..............................................................................................................................14

Vehicle Speed................................................................................................................................................16

Vehicle Lateral Position ................................................................................................................................16

Yaw/Brake/Acceleration Activity .................................................................................................................16

Summary of Suitability of Vehicle Performance Output for Drowsiness Detection.....................................17

3. TRUCK DRIVING SIMULATOR LABORATORY.......................................... 18 

SIMULATOR REQUIREMENTS....................................................................................................19

Review of Existing Simulators / Performance Objectives of the Simulator..................................................19

Driving Simulation Challenges.....................................................................................................................21

INTEGRATION OF THE TRUCK SIMULATOR ........................................................................23

INRETS Driving Simulation Environment ...................................................................................................24

Considerations Specific to Commercial Vehicles.........................................................................................25

Hardware .......................................................................................................................................................26

Validation of the Simulator...........................................................................................................................37

i 
4. EXPERIMENT AND DATA COLLECTION...................................................40 

OVERVIEW OF THE DROWSY DRIVING EXPERIMENT......................................................40

Study Population ...........................................................................................................................................40

Equipment .....................................................................................................................................................41

Driving Scenario ...........................................................................................................................................41

Experimental Protocol...................................................................................................................................41

DATA COLLECTION.......................................................................................................................43

Vehicle Parameter Data.................................................................................................................................43

Eye Closure Data...........................................................................................................................................43

Video Images.................................................................................................................................................44

DATA ANALYSIS .............................................................................................................................44

Identification of Driver Drowsiness..............................................................................................................44

Eye Closure Data (PERCLOS)......................................................................................................................46

Correlation of Performance Variables with Drowsiness Development.........................................................47

Video Recording of Driver Behavior ............................................................................................................87

Data Analysis Summary................................................................................................................................90

5.

 .........................................................................................................92 

DEVELOPMENT OF ALGORITHMS FOR DRIVER DROWSINESS DETECTION

ARTIFICIAL NEURAL NETWORK MODELS............................................................................93

Training Algorithm (Supervised ANN).........................................................................................................94

Training Parameters ......................................................................................................................................95

Cross Validation............................................................................................................................................97

ANN MODEL BASED ON STEERING ANGLE DATA...............................................................98

DATA PREPROCESSING AND INPUT DISCRETIZATION (STEERING ANGLE) .............98

Effect of Road Curvature and Lane Changes................................................................................................98

Input Discretization (Steering Angle) .........................................................................................................101

Input Coding (Steering Angle)....................................................................................................................103

Input Data (Steering Angle) ........................................................................................................................104

Desired Output ............................................................................................................................................104

ANN ARCHITECTURE (STEERING ANGLE) ..........................................................................105

TRAINING DATA SET (STEERING ANGLE) ...........................................................................106

ANN TRAINING (STEERING ANGLE) ......................................................................................106

ANN TESTING (STEERING ANGLE).........................................................................................108

ANN MODEL BASED ON STEERING AND EYE DATA .........................................................108

EYE CLOSURE DATA ...................................................................................................................109

Input Discretization (Eye Data)...................................................................................................................109

Input Coding (Eye Data) .............................................................................................................................112

Input Data (Steering + Eye).........................................................................................................................112

ANN ARCHITECTURE (EYE+STEERING)...............................................................................114

ii 
iii 

TRAINING DATA (EYE+STEERING).........................................................................................114

ANN TRAINING (EYE+STEERING) ...........................................................................................114

ANN TESTING (EYE+STEERING)..............................................................................................115

RESULTS AND DISCUSSION FOR TESTING ALL SUBJECTS ............................................116

CRASH PREDICTION RESULTS ................................................................................................134

SUMMARY AND COMPARISON WITH PREVIOUS DATA ..................................................134

6. DRIVER WARNING................................................................................137 

WARNING SYSTEMS CHALLENGES .......................................................................................138

System Effectiveness...................................................................................................................................138

User Acceptance and Trust..........................................................................................................................138

Issuance of False Alarms.............................................................................................................................139

Training Issues ............................................................................................................................................140

Issues of Warning Understanding ...............................................................................................................140

WARNING STRATEGIES .............................................................................................................140

Warning Content .........................................................................................................................................140

Type of Warning .........................................................................................................................................141

Warning Timing ..........................................................................................................................................141

Warning Modality .......................................................................................................................................142

REMAINING ISSUES AND RESEARCH OPPORTUNITIES ..................................................145

Driver Behavioral Adaptations....................................................................................................................145

Public Attitudes...........................................................................................................................................146

Interaction with Other Safety Systems........................................................................................................146

Evaluation of a Driver Drowsiness Warning System in a Driving Simulator .............................................146

7. CONCLUSIONS AND FUTURE WORK.......................................................147 

APPENDIX A. RESULTS FROM PREVIOUS ANN MODELS...............................150 

APPENDIX B. 

162 

DESIGN, FABRICATION, AND ADAPTATION OF HARDWARE 

ELEMENTS FOR CISR TRUCK SIMULATOR.............................................

LIST OF REFERENCES................................................................................0 

 
EXECUTIVE SUMMARY 

This report summarizes the findings of the Advanced Driver Fatigue Research project conducted 

by the Center for Intelligent Systems Research (CISR) of the George Washington University 

(GWU), and funded by the Federal Motor Carrier Safety Administration (FMCSA) of the U.S. 

Department of Transportation (USDOT). The goal of this project was to develop an unobtrusive 

drowsy driver detection system for commercial motor vehicles. CISR previously developed an 

effective drowsiness detection system for automobiles that relied solely on the driver’s steering 

patterns for input. This project’s evaluation of this system confirms that this approach is equally 

valid for truck drivers, despite the differences between truck and automobile driving. The 

drowsy driver detection system exhibited acceptable false positive and false negative readings 

and an ideal warning rate before crashes. 

A system that relies solely on steering inputs provides a number of benefits over the more 

common means of detecting drowsiness through eye-tracking. A steering-only detection system 

is unobtrusive, capable of being implemented inexpensively with a minimal amount of additional 

sensors and computing power, and immune to problems associated with eye-tracking systems 

such as performance degradation under low-light conditions or when drivers wear glasses. A 

steering-only system is based on the hypothesis that people steer differently when they are 

drowsy. Drowsy driving is marked by a lower vigilance in lane keeping which leads to fewer 

micro-steering corrections and more macro-steering corrections. Given the variability in driving 

styles and human behavior, a precise model of fatigued steering behavior is extremely difficult to 

develop. However, in previous studies, CISR has successfully used Artificial Neural Networks 

(ANN) to successfully learn patterns of fatigued and non-drowsy steering. 

After conducting a literature review of previous research and studies, CISR developed a full-size, 

fixed-base truck driving simulator laboratory, in partnership with the French National Institute 

for Transport and Safety Research (INRETS). Experiments were then conducted using fourteen 

drivers, with both truck driving experience and valid commercial driver’s licenses, who 

participated in the experiment which consisted of three driving sessions for each subject. Each 

driver participated in a practice session, and then a morning session followed by a late-night 

driving session. During this night session, most of the subjects experienced one or more 

episodes of drowsiness. 

Two ANN systems were developed to detect driver drowsiness. One of the systems included 

inputs of both eye closure and steering activity, while the other ANN used only steering activity. 

Both the steering-with-eye-tracking ANN and the steering-only ANN performed well, at levels 

analogous to the previous passenger vehicle detection systems. The steering-only system had an 

accuracy of 85% in predicting drowsy intervals with a false alarm rate of 14%. Moreover, it 

issued a timely warning in 100% of the collisions that were studied in this project. 

The last component of this project involved research into viable drowsy driver detection warning 

systems. A review of existing literature into drowsy driver warning systems and ITS warning 

systems, in general, indicated the development of the design goals for a proposed alarm system. 

The timing, intensity, and nature of the warning system to be integrated as part of the study with 

the detection algorithm are discussed in this report. 

1
1. INTRODUCTION 

This report is submitted by the Center for Intelligent Systems Research (CISR) of the School of 

Engineering and Applied Science (SEAS) of the George Washington University (GWU) to the 

Federal Motor Carrier Safety Administration (FMCSA) of the U.S. Department of 

Transportation (USDOT). This is the final document and the last of the series of reports that 

were submitted throughout the duration of the project. 

The goal of this project was to develop a drowsy driver detection system for commercial motor 

vehicles. The goal of this project was met through two specific objectives. The first objective of 

this study was to arrive at an experimentally validated system for fatigue detection in commercial 

motor carriers. By monitoring the driver’s steering activity and eye closure behavior, the aim for 

this system is to be able to detect a state of driver drowsiness or fatigue sufficiently early to take 

preventive measures against potential crashes. The emphasis in this project was on development 

of an algorithm for an unobtrusive monitoring system which mitigates the need for eye tracking 

as much as possible. A second objective was to research viable countermeasures, for example, 

warning systems that minimize the severity of crashes and reduce injuries and fatalities in 

highway crashes involving trucks. 

In previous studies, CISR developed an unobtrusive drowsiness detection system that performed 

extremely well in experiments with car simulators. This system monitored steering activity to 

assess a driver’s level of alertness. The underlying hypothesis for this system is that driver’s 

steering activity is affected by drowsiness. A drowsy driver will tend to make fewer microsteering corrections and compensate with more macro-steering corrections. The variability in 

driving behavior both for one person at different times and across multiple drivers precluded the 

development of a precise analytical, model of steering behavior. However, an Artificial Neural 

Network (ANN) system was able to learn the relationship between drowsiness and steering 

behavior. In previous experiments, this system detected the onset of drowsiness for car drivers 

with a very high degree of accuracy. 

However, it was not clear whether this system would also work for drowsiness detection in large 

commercial vehicles. Significant differences include the additional experience and training of 

truck drivers, the dynamics of the trucks versus cars, and the different feel of the steering 

systems in trucks and cars. Consequently, altered driver behavior and steering signals might 

limit the effectiveness of this approach for tractor trailers. 

These differences notwithstanding, this project demonstrated that this approach is equally 

applicable for the detection of drowsiness in truck drivers. The effectiveness of the system for 

truck drivers was tested in simulator-based experiments. A truck driving simulator was 

constructed for the purpose of this project. Designed to produce the realism required for the 

drowsy driver experiments, this simulator included a full-size cab, 135 degree forward field of 

view, projected scenes for both side mirrors, force feedback steering wheel, a gearbox, and 

realistic action for the accelerator, brake, and clutch pedals. The simulated roadway was based 

on I-70 in the state of Kansas. The simulator includes extensive data acquisition equipment, 

including instrumentation of all of the controls in the cabin, driver eye tracking, and video 

2
recording of the subjects from multiple camera angles. Before the experiments were conducted, 

the simulator was validated for realism by both truck drivers and experts in truck simulators. 

Fourteen drivers, with both truck driving experience and valid commercial driver’s licenses, 

participated in the experiment which consisted of three driving sessions for each subject. First, 

the subjects participated in a practice session, which lasted approximately one hour, in which 

they became accustomed to driving the simulator. Sometime later, usually the next day, the 

subject would then participate in a one-hour morning session, which was held between 8:30 and 

9:30 am. The purpose of the morning session was to establish a baseline of predominantly nondrowsy steering behavior for each subject. During the day, the subject was instructed to avoid 

napping and excessive caffeine consumption. That night, a member of the research team would 

drive the subject from their abode to the simulator. The subject would then participate in a night 

driving session starting around 1:30 am, with each session lasting for approximately two hours. 

During this night session, most of the subjects experienced one or more episodes of drowsiness. 

The data generated from these experiments was analyzed thoroughly to evaluate inputs for a 

drowsy driver detection system and performance metrics for the system. This analysis showed a 

correlation between high amplitude steering corrections and drowsiness. This analysis also 

indicated that the crashes are strongly correlated with drowsiness. Due both to this correlation 

and the primary purpose of the drowsy driver warning systems, collision avoidance, the ability of 

the drowsiness detection system to detect drowsiness in a timely fashion prior to a collision was 

established as the most important evaluation criterion. 

Two ANN systems were developed to detect driver drowsiness. One of the systems included 

inputs of both eye closure and steering activity, while the other ANN used only steering activity. 

A small portion of the data from the morning and night sessions was extracted for training and 

cross validation of these ANNs. The performance of the systems was then evaluated with the 

remaining data. A key component of both ANNs is the data preprocessing that discretizes the 

steering signal into a format that is designed to produce a generalized signal that removes the 

variations in steering strategies across different drivers and different driving platforms. 

Both the steering-with-eye-tracking ANN and the steering-only ANN performed well at levels 

analogous to the previous passenger vehicle detection systems. The steering-with-eye-tracking 

ANN had an accuracy of 88% with a false alarm rate of 9%. The steering-only ANN had an 

accuracy of 85% with a false alarm rate of 14%. 

The steering-only ANN performed extremely well in the most important evaluation criterion, the 

ability to timely detect and warn drivers before a collision due to drowsy drivers. Although all 

the crashes were examined, the first two crashes for each subject are the most relevant, because 

the driver may remain drowsy through multiple later crashes. The system issued a warning in a 

timely fashion for 100% of the first two crashes experienced by any of the subjects. On average, 

5.6 warnings were issued during the 5 minutes preceding the crash, with the first warning 

occurring on average 3 minutes and 56 seconds prior to the crash. 

Given the success of this system in the simulator experiments, this drowsy driver detection 

system is ready for development as a prototype system with sensors and computing resources 

needed for track tests and field operational tests. Since this approach was relatively successful in 

3
4

three different sets of simulators with three different simulators, this approach to drowsy driver 

detection should translate well to real driving conditions. 

Chapter 2 of this report consists of a comprehensive review of previous literature and existing 

systems for the purpose of incorporating the relevant previous findings, eliminating duplication 

of efforts, and examining the earlier proposed detection and warning systems, with a particular 

emphasis on truck driving conditions. Factors that contribute to fatigue/drowsiness, measures to 

counter driver fatigue/drowsiness, previous studies, and surveys are discussed in detail. This 

chapter corresponds to TASK-1 of this study. 

Chapter 3 describes the development of a truck driving simulator laboratory at the CISR, which 

was used to conduct the experiments for this project. CISR, in agreement with FMCSA, decided 

to set up a new low-cost truck driving simulator in partnership with the Modeling, Simulation 

and Driving Simulators (MSIS) research unit of the French National Institute for Transport and 

Safety Research (INRETS). This new simulator is dedicated to truck driving simulation tasks. 

CISR constructed the new truck-driving simulator at the GWU campus in Ashburn, VA. Full 

details about the capabilities, working, and activities performed during construction and 

assembling of the simulator are discussed. This chapter corresponds to TASK-3 of this study. 

Chapter 4 describes the simulator experiments conducted at the CISR truck driving simulator 

laboratory. During these experiments, truck drivers drove the truck simulator under different 

levels of fatigue and sleep deprivation. Data was recorded for parameters related to driver eye 

closure and driving activity. Data recorded in these experiments was used to develop and 

validate the detection algorithm. 

In this study, an ANN was trained to learn the steering behavior of drivers driving under 

different levels of sleep deprivation in order to identify drowsy driving behavior. The success of 

the method depends on a unique data-preprocessing scheme, which enables the use of a single 

ANN for all drivers by incorporating the variability in human driving behavior. Details of the 

model development and results of the model performance are discussed in Chapter 5. Chapters 4 

and 5 correspond to TASK-4 of this study. 

The detection algorithm (or system) analyzes data for a specific time interval and then classifies 

that interval as wake or drowsy. However, when and how should a system warn a driver about 

the hazardous situation is another topic for driver/human factor research. If the system waits too 

long, the driver may not have enough time to react and prevent an accident. At the same time, if 

the alarm goes off when the likelihood of an accident is very low, the driver may get annoyed 

and ignore the warning or disconnect the system. Similarly, if the alarm is too low, the driver 

may not notice it and if it is too intense, it may cause panic. Chapter 6 details the proposed alarm 

system. The timing, intensity, and nature of the warning system to be integrated with the 

detection algorithm are discussed. This system builds upon the existing data and knowledge 

gained through previously reported driver warning studies. This section corresponds to TASK-5 

of this study. 

Appendix A describes the results of previous experiments, which corresponds to TASK-2 of this 

study. Appendix B provides details of simulator lab development. A list of references cited in 

the report is also provided. 
2. LITERATURE REVIEW 

The main objective of this section is to search and review literature related to the 

detection of driver fatigue and drowsiness. The specific tasks that were performed to 

meet this objective include: 

• Search all the research performed in the field of driver fatigue/drowsiness 

• Review the performance and merits and shortcomings of available fatigue/drowsy 

detection systems 

• Review any specific studies or data pertaining to truck driver fatigue/drowsiness 

• Review any DOT or driving simulator data available, through FMCSA or other 

sources 

The literature review is organized by the type of technology and methods used for 

detecting fatigue/drowsiness. A detailed description on the background of the problem of 

driver fatigue/drowsiness is provided at the beginning. Statistics on the problem size and 

scope are provided. Various factors that affect driver alertness and causes of driver 

fatigue are reviewed. 

PROBLEM BACKGROUND 

In a 1991 report, the Office of Crash Avoidance Research (OCAR), part of the National 

Highway Traffic Safety Administration (NHTSA), identified driver drowsiness as one of 

the leading causes of single and multiple car accidents. NHTSA estimates that 100,000 

crashes annually involve driver fatigue resulting in more then 40,000 injuries. The 

Fatality Analysis Reporting System (FARS) estimates that 1,544 fatalities are due to 

driver drowsiness related accidents, each year; however, the actual impact of driver 

drowsiness is most likely much worse than these statistics would indicate. Often it is 

very difficult to attribute drowsiness as a cause to an accident due to a dearth of physical 

evidence that would implicate drowsiness. Indeed, it is widely believed that there are a 

large number of accidents in which the initial cause of the loss of vehicle control is 

drowsiness but is reported as something else. Recent research confirms that the problem 

of drowsy driving is indeed more serious than was earlier perceived. 

Researchers at Duke University conducted a survey to study the problem of drowsiness in 

drivers (Tilley et al, 1973). They distributed 1,500 questionnaires at the Durham, North 

Carolina Department of Motor Vehicle Administration. Out of the 1,500 drivers, 64% 

responded that they had become drowsy at some time while driving. More than 7% said 

they had fallen asleep while driving for short periods of time. Out of the 64% who 

responded that they had experienced drowsiness while driving, more than 31% said they 

were not aware of their condition before they actually became drowsy; 10% of those who 

experienced drowsiness said they had been involved in one or more accidents due to 

5
drowsiness or falling asleep. Another 10% said they had been in a near accident situation 

due to drowsiness. 

The Kanagawa, Japan Prefectural Police also conducted a survey on driver fatigue (Seko, 

1984). According to the findings of this survey, 75% of the drivers admitted that they 

had driven while drowsy. Seko also reported about another survey conducted by the 

Shizuoko Prefectural Police in 1973. According to that report, 9% of all accidents were 

caused by drowsiness, and 45% of all traffic fatalities were due to accidents caused by 

driver drowsiness. According to Planque et al (1991), fatigue is the cause of 26% of the 

fatal accidents on the highways of France. 

Some of the major findings of the OCAR 1991 report on crash statistics were: 

• According to police reports, there were 72,000 crashes (1.1 percent) annually in 

which driver drowsiness was cited as the primary cause. 

• Annually, driver drowsiness was involved in about 14,000 accidents that cause 

serious injuries (2.9 percent of all serious injuries). 

• Accidents involving driver drowsiness caused 1,550 fatalities (3.4 percent of all 

fatalities). 

• These accidents were under reported and the actual number of accidents involving 

driver fatigue was greater. The Duke, Japanese, and French surveys indicate that 

the magnitude of this underreporting may be on the order of 10. 

Fatigue has been estimated to be involved in 2% to 23% of all crashes (O’Hanlon 1978, 

Horne and Reyner, 1995), in 4% to 25% of single vehicle crashes (Wang and Knipling, 

1994), in 10% to 40% of crashes on long motorways (Shafer, 1993; Dinges, 1995), and in 

15% of single vehicle fatal truck crashes (Wang and Knipling, 1994). Fatigue has been 

implicated as the most frequent contributor to crashes in which a truck driver is fatally 

injured (U.S. National Transportation Safety Board [NTSB], 1990). 

Crashes that involve a driver falling asleep are on average very serious in terms of injury 

severity (Pack et al, 1995) and are more likely to occur in sleep-deprived individuals. 

Accidents involving driver drowsiness have a high fatality rate because the perception, 

recognition, and vehicle control abilities of the driver reduces sharply while falling 

asleep. It has been shown by researchers that subjects can not predict when they will 

have a serious sleep attack (Wylie et al, 1996; Brown, 1997). Driver drowsiness 

detection technologies may have the ability to avoid a catastrophic accident by warning 

the driver of his/her drowsiness. 

FACTORS CONTRIBUTING TO FATIGUE RELATED ACCIDENTS 

Researchers have identified many factors that can be attributed to the causes of 

drowsiness related accidents. Factors that influence driver fatigue/drowsiness include 

greater daytime sleepiness, less sleep, more difficult schedules, more hours of work, a 

6
driver’s age, driver experience, cumulative sleep debt, the presence of a sleep disorder, 

and the time of day of the accident (Gander and James, 1999; McCartt et al, 2000). 

Sleep Deprivation 

The most common cause of drowsiness is a lack of sleep. The effect of sleep deprivation 

is cumulative and loosing one or two hours of sleep a night can accumulate to cause 

serious sleep deprivation overtime (Carskadon and Derment, 1981). Sleep disruptions or 

fragmented sleep causes a loss of sleep and will result in sleep deprivation (Dinges, 

1995). Indeed, the loss of one night of sleep can result in extreme sleepiness (Carskadon, 

1993). 

Many studies have reported the adverse effect of sleep loss on driving. The findings of 

these studies include: 

• Sleeping less then 4 hours per night severely impairs driving performance 

(Naitoh, 1992). 

• Drivers averaging less then 5 hours of sleep per night increase their risk of being 

in a sleep related crash nearly five times (Stutts, 2003). 

• Loss of sleep increases the tendency of falling asleep and reduces driving 

performance (Wilkinson et al, 1966; Mitler et al, 1997). 

• A 1995 NTSB report identified the duration of the driver’s last sleep period, the 

total sleep obtained during the 24 hours preceding the crash, and fragmented sleep 

patterns as the most important contributing factors towards a drowsiness related 

single vehicle large truck crashes. 

• According to Sweeney et al (1995), the two most important contributing factors in 

distinguishing between fatigue and non-fatigue related accidents are the duration 

of the last sleep period and the amount of sleep in the last 24 hours. The duration 

of continuous wakefulness, acute sleep loss, and the cumulative sleep debt 

contribute significantly to fatigue related accidents. 

Work Schedule 

Many studies point to a driver’s work schedule as having a significant impact on driver 

fatigue, particularly for commercial vehicle operators: 

• Researchers like Lin et al (1994) have reported that the total driving time greatly 

increase the crash risk. 

• According to one experimental study, driving performance among truck drivers 

start declining after 5 hours of driving for drivers with irregular schedules as 

compared to 8 hours for drivers with regular schedules (Mackie and Miller, 1978). 

• Harris et al (1972) found that the driver risk of being involved in a crash increases 

after 4 hours of continuous driving. 

7
• Tractor-trailer drivers who violated the hour of service rule are more likely to fall 

asleep at the wheel (Braver et al, 1992). 

• Truck drivers, who split their 8 hours of required off duty time into two shifts, are 

at increased risk of being involved in a fatal crash (Hertz, 1988). 

• Lavie (1986) argues that truck drivers, who work irregular night shifts and are 

compelled to sleep during the day time, may not be getting the restorative quality 

of night time sleep. 

• Factors associated with sleep-related crashes include working two or more jobs, 

working night shifts and working more then 60 hours per week (Stutts, 2003). 

Sleep Disorder/Quality of Sleep 

Sleep disorders can also have a significant impact on driver performance: 

• Individuals with sleep apnea and other sleep disorders that cause excessive 

daytime sleepiness are at high risk for accidents (Stoohs et al, 1993; Young et al, 

1997). 

• Drivers involved in sleep crashes are more likely to report that they often or 

always had problem falling or staying asleep. They are also more likely to report 

that the overall quality of their sleep is poor (Stutts, 2003). 

Time of Day 

Sleep accidents are more likely to occur during the early morning hours from 2 am to 6 

am and to a lesser extent during the afternoon from 2 pm to 4 pm. Both Mackie and 

Miller (1978) and Harris (1972) found that a correlation exists between time of day and 

level of fatigue. Due to circadian rhythms, people feel sleepiness during the afternoon 

and evening hours, even among people who are not sleep deprived (Dinges, 1995). 

Driver schedules that interfere with natural circadian sleep patterns can disturb sleep. 

Truck drivers who drive during night time are at higher risk of being involved in a crash 

(Hertz, 1988; Jovanis et al, 1991). 

Monotony of Road/Driving Conditions 

A situation is said to be monotonous when the stimuli remain unchanged or change in a 

predictable manner (McBain, 1970). The monotony of the roadway geometry and 

environment has been implicated as a cause of driver drowsiness by a number of studies: 

• Due to the monotony of highways, highway night drivers are particularly 

vulnerable to sleep related accidents (Akerstedt and Kecklund, 1994). 

• Sleep related accidents may be more common on long stretches of interstate 

highways and may account for 40% of fatal accidents (Shafer, 1993). 

8
• Driving performance degrades at a faster rate on straight road sections than on 

curves (Desmond and Mathews, 1998). 

• 40% of sleep related accidents occur on highways (McCartt et al, 1996). 

• According to one estimate, 30% of accidents on rural roads are due to driver 

drowsiness (Fell, 1994). 

• In a self reported driver fatigue/drowsiness study, U.S. drivers were found to be 

more prone to drowsy driving as compared to Norwegian drivers (Sagberg, 1999). 

Sagberg argues that the cause may be the geometry and environment of U.S. 

highways. He further argues that the risk of falling asleep is higher on straight, 

monotonous roads with low traffic, where boredom is more likely to occur. 

• A simulator study suggested that fatigue is likely to occur much earlier when 

driving in a monotonous, low demanding road environment (Thiffault and 

Bergeron, 2003a). 

Driver Personality and Age 

Research has also found a driver’s personality and age have a large influence on the 

susceptibility to drowsiness: 

• Many studies have found that there are large individual differences in the 

susceptibility to become drowsy while driving (Verwey and Zaidel, 2000; Artaud 

et al, 1994; Wylie et al, 1996). They all argue that some drivers are more affected 

by study conditions than others. 

• Thiffault and Bergeron (2003b) found that drivers who are more sensation seekers 

may be more sensitive to road monotony and thus more prone to fatigue related 

driver errors on monotonous, low demanding roads. 

• Campagne et al (2004) compared the performance of three age groups in a driving 

simulator study and found that deterioration of vigilance is correlated with driving 

errors for drivers aged 60 and above. 

CHARACTERISTICS OF DROWSINESS/FATIGUE RELATED 

ACCIDENTS 

Horne and Reyner (1995) identified criteria by which sleep related vehicle accidents 

could be identified. These criteria include vehicle running off the road, no sign of 

braking, no mechanical defect, good weather, and elimination of speeding. NHTSA 

reported the following statistics on crashes indirectly related to the driver 

drowsiness/sleepiness: 

9
• The highest number of crashes occurs during the period from midnight to early 

morning. More than 40% crashes occur between 1 am and 7 am. The probability 

of falling asleep is very high during this time interval. 

• About 70% of crashes occur on rural highways with 55 to 65 mph speed limit. 

This provides a monotonous and calm atmosphere, which is just right for falling 

asleep. 

• The first events that occur in the accident are: 

o 64% are collisions with fixed objects (trees, guardrail, highway sign, etc.), 

which is another characteristic of sleepy drivers 

o 17% are collisions with another moving vehicle 

o 7% are rollover 

o 6% are collisions with parked vehicles 

Crashes related to driver fatigue were classified by Hamouda and Saccomono (1995) who 

developed an ANN model for identifying patterns in fatigue related accidents. The ANN 

used various data available from police accident reports to determine if a crash was 

caused by driver drowsiness or by other non-drowsiness related factors. Similarities in 

the conditions of drowsiness related accidents could then be identified. The ANN was 

trained on data from police reports and the database of the province of Ontario. 

EARLY ATTEMPTS AT DETECTING DRIVER DROWSINESS 

Early driver drowsiness research used various methods for detection and warning 

systems. Some of the mechanical devices that were available in the mid-1960s and early 

1970s include the following (Hulbert, 1972): 

• The electronic Transistor Safety Alarm was a lightweight plastic device that 

curled around the driver’s ear and buzzed when the driver’s head nodded. 

• The Button Steering Wheel Alarm plugged into the car and was mounted on the 

steering wheel. An alarm will sound any time the button was released. The 

device was deemed very impractical. 

• The ALERTMASTER was a pedal positioned on the floor to the left of the clutch 

pedal. Anytime the pedal pressure was released, the horn would sound. 

• The Alert-O-Matic was based on the driver response to a light signal presented 

every 60 seconds. If the driver response (tapping on the horn) was not adequate, 

an alarm would be set off. 

10
PREVIOUS STUDIES 

Previous studies have sought to develop algorithms for the detection of drowsiness in 

drivers. Many of these studies have used cameras or human observers to monitor eye 

closure for this purpose. 

Dingus et al (1985) performed a study to observe the effects of drowsiness on driver 

performance. They used data from subjects under rested and sleep deprived conditions 

and developed an algorithm called PERCLOS, which is a measure of the proportion of 

time that the eyes of a subject are closed over a certain time period, as judged by a human 

scorer. They reported that best results could be obtained when the performance variable 

used is the proportion of time when the driver eyes are 80 to 100 percent closed. Dingus 

et al used eyelid closures as a definition of drowsiness to analyze different performance 

variables. They performed a correlation analysis between the measure of eyelid closures 

and other vehicle and driver related measures and reported that a relatively high 

correlation exists between eyelid closures and lane position measures. 

Wierwille et al (1994) conducted a study to determine what variables and combination of 

variables could be used for the prediction of driver drowsiness. In this study, subjects 

drove a driving simulator both under rested and sleep deprived conditions. Numerous 

driver, vehicle, and secondary task performance measures were collected. The measures 

used in this study were related to seat movement, steering movements, vehicle lateral 

position, accelerator pedal movements, heading angle, subsidiary task, EEG, and heart 

rate. Wierwille used PERCLOS and an average drowsiness rating of three observers for 

each six-minute time interval as measure for drowsiness. To determine the best predictor 

of drowsiness, Wierwille developed numerous algorithms using multiple regression and 

discriminant analysis methods. He concluded that no one model will be sufficient under 

all circumstances and good accuracy could only be achieved by using a combination of 

models. 

SENSING DRIVER’S PHYSICAL AND PHYSIOLOGICAL 

PHENOMENON 

In the scientific literature, researchers have approached the problem of driver drowsiness 

detection by using different techniques. These techniques can be broadly classified into 

two categories based on sensing of driver physical and physiological phenomena and 

sensing performance output of the vehicle hardware. Each of these techniques is detailed 

in this section. 

A variety of methods are used to sense the physical and physiological phenomena of 

drivers. These include techniques such as detection by analyzing changes in brain wave 

or EEG, eye activity, facial expressions, head nodding, body sagging posture, heart rate, 

pulse rate, skin electric potential, gripping force on steering wheel, and other changes in 

body activities. 

11
Eyelid Closure 

Eyelid closure is one of the most obvious approaches to monitoring driver drowsiness. A 

number of different techniques are available for obtaining this measure. 

Erwin (1976) studied various measures to determine whether they were predictive of 

sleep and reported eyelid closure as the most reliable predictor of the onset of sleep. 

Haider and Rohmert (1976) evaluated blink rate, while subjects drove a truck simulator 

for 4 hours, and reported an increase in the blink rate between 80 to 100%. It seems quite 

obvious that if a driver eyes are closed, the ability to drive a vehicle will be greatly 

reduced. 

Skipper et al (1984) studied the performance of sleep deprived drivers, who performed a 

1 1⁄2 hour driving task. The experimenters used a linear potentiometer to manually track 

the eyelid movements of the drivers. The researchers concluded that eyelid closures 

could be used as a measure for detecting drowsiness 

Ogawa and Shimotani (1997) analyzed data from a driving simulator experiment. They 

concluded that long duration blinks of half a second or more corresponds to subjective 

evaluation of sleepiness. The authors define a degree of alertness (α) as 

α = number of long duration blinks/total number of blinks. 

The driver was considered drowsy every time the value of α increased above a specified 

threshold value. 

Different techniques have been used to track the eyelid closures. In the Dingus et al and 

the Hardee et al studies, experimenters manually track the eyelid movements. By 

looking at the video image on a monitor, an experimenter used a linear potentiometer to 

track the position of the eyelids. 

Ueno et al (1994) used a method based on the Feret’s diameter of the eye to track the 

eyelid closures. The system analyzes the image of driver face taken by a video camera. 

After separating the eyes from the rest of the facial features, it defines a rectangular 

window around the eye on the basis of the Feret’s diameter of the eye. The maximum 

number of black pixels along the vertical axis of the window indicates the degree of eye 

openness and is used, as a basis for judging whether eyes are open or closed. 

Electrooculography (EOG) involves the measuring of eye movements through electrodes 

attached to the skin surrounding the eye. The detection of the eye movements is 

satisfactory only when the movements are visually unambiguously definable and isolated. 

Failure rate is very high for atypical eye movements. 

Ogawa and Shimotani (1997) used the angle of inclination of eye corners to track the 

eyelid closures. This angle is steep when eyes are open and shallow when eyes are 

closed. 

Seki et al (1997) developed a method that uses reflection from the retina (bright pupil) to 

determine whether the eyes are open or closed. In this method, a charge-coupled device 

12
(CCD) camera, using an infrared light to illuminate a driver’s face, captures the driver’s 

facial image, which is then converted into digital images. The pupils are identified on the 

basis of their geometric features and relative positions using the binary image. The eyes 

are considered closed when there is no reflection from the retina. 

Electroencephalogram 

The Electroencephalogram (EEG) recorded from the human scalp is the most important 

physiological indicator of the central nervous system activation and alertness. Many 

researchers have used this physiological indicator to identify the period of drowsiness. 

From a state of fully awake to a state of fully asleep, the EEG varies in frequency bands 

ranging from 0 to 20 hertz. These frequency bands are classified as follows: 

• Delta waves ranging from 0 – 4 hertz 

• Theta waves ranging from 4 – 8 hertz 

• Alpha waves ranging from 8 – 12 hertz 

• Beta waves ranging from 13 – 20 hertz 

An alert mental state is accompanied by fast frequency beta activity, whereas a sleep state 

is accompanied by slower theta activity. Alpha activity is associated with relaxed 

experience during which attention is unfocused, showing drowsiness. 

Researchers have proposed various methods to extract features from a segment of the raw 

EEG. In the time domain, average value, standard deviation, and sum of squares of EEG 

amplitude are most commonly used. In frequency domain, energy content of each band 

(β, α, θ, δ), mean frequency, and center of gravity of the EEG spectrum are commonly 

used. Other models, such as Auto Regressive Moving Average (ARMA) and power 

spectrum estimation, are also used by some researchers to extract EEG features. 

Torsavall and Akerstedt (1987) measured continuous EEG spectra for 11 train drivers. 

They reported that lapses of attention were preceded by increase in low frequency EEG 

activity. The researchers also showed that the driver vigilance tends to diminish rapidly 

after prolonged driving and can be measured by means of spectral analysis of the EEG. 

Richardson et a. in the European PROMETHEUS project used EEG in conjunction with 

other variables to find correlation between drowsiness and EEG. Alkerstedt and Gilberg 

(1990) and Huang et al (1996) used fluctuations in mean frequency of EEG to detect the 

state of alertness. Generally, EEG is considered suitable for making accurate and 

quantitative judgments of alertness levels. Wierwille and Ellsworth (1994) found that 

trained observers could rate the drowsiness level of drivers based on video images of 

driver faces. 

13
SUITABILITY OF VEHICLE PERFORMANCE OUTPUT FOR 

DROWSINESS DETECTION 

An EEG can provide very good detection accuracy, as it is a direct measure of the 

activity of the central nervous system. However, the problem with an EEG is that it 

requires the use of electrodes to be attached to the scalp, which makes it very impractical. 

Eye closure activity can also provide good detection accuracy but capturing eye image 

unobtrusively can be challenging under certain conditions. Changes in light conditions, 

correction glasses, angle of face, and other conditions can seriously affect the 

performance of image processing systems. 

Other approaches for detecting driver drowsiness are based on monitoring driver inputs 

or vehicle output variables during driving. These methods have the advantage of being 

non-intrusive to the drivers. In this category, the focus of measurement is not on the 

condition of driver but it is on the performance output of the vehicle hardware. The 

vehicle control systems that might be monitored for sensing driving operation include the 

steering wheel, accelerator, and brake pedal. The vehicle parameters that can be 

measured include the vehicle speed, acceleration, yaw rate, and lateral displacement. 

Since these techniques allow non-contact detection of drowsiness, they do not give the 

driver any feeling of discomfort. On the negative side, they are subject to numerous 

limitations depending on the vehicle type and driving conditions. Wierwille et al (1992) 

discussed the performance measures as indicators of driver drowsiness in detail. A 

summary of these measures is presented in the following sections. 

Vehicle Steering Activity 

For many years, experiments have been carried out to determine the physical parameters 

characterizing driving, which could be correlated with EEG parameters that can predict 

the driver drowsiness. Vehicle steering activity has been cited by many of these studies. 

Hulbert (1972) found that the sleep-deprived drivers have a lower frequency of steering 

reversals (every time the steering angle crosses zero degrees) than that of rested drivers. 

Researchers like Mast et al (1996) and Dureman and Boden (1972) have found that there 

is a deterioration of steering performance with drowsiness. 

According to Kahneman (1973), effort and Steering Wheel Reversing Rate (SWRR) are 

linked. He showed that the SWRR decreases under the influence of substances such as 

alcohol, which reduces driver activation level. Ryder et al (1981) found that the 

frequency of steering reversals decreases with time on task. 

Yabuta et al (1985) hypothesized that when a driver is drowsy or falling asleep, his/her 

steering behavior becomes more erratic. Yabuta defined this erratic steering behavior as 

“more frequent steering maneuvers during wakeful periods, and no steering correction for 

a prolonged period of time followed by a jerky motion during drowsy periods.” 

Dingus et al (1985) found that several steering related measures, such as steering 

velocity, steering wheel increment, and low velocity steering, can be used to predict 

drowsiness. 

14
Mackie and Wylie (1991) provided a review of patterns of steering wheel movements and 

vehicle speed. They have affirmed the complexity of the analysis of these two variables 

and reported that the environmental factors could highly affect the steering precision. 

A study conducted by Chaput et al (1990) suggests that there exists some correlation 

between micro steering movements and drop in vigilance. During high vigilance (alert) 

periods, small amplitude steering wheel movements are frequent, but during fatigued 

periods, large amplitude movements are more visible. 

Elling and Sherman (1994) analyzed actual driving data from one hour of continuous 

driving by professional drivers. They reported that steering wheel reversals and standard 

deviation of steering wheel angle are two measures that show some potential as 

drowsiness indicators. They also reported that gap-size (i.e., the angle that the steering 

wheel must be reversed before being counted as a reversal) has a major influence on the 

reversal rate. Their gap-size function has a dead-band that disregards any extremely 

small reversals such as those due to road variations. 

Fukuda et al (1995) developed a driver drowsiness detection system at the Toyota Motor 

Company. The authors used steering adjustment time to estimate drowsiness. Their 

method consists of the following: 

• Steering adjustment intervals are calculated at different speeds for alert conditions 

(learning). These intervals vary with speed and individual behavior but it follows 

the same pattern. 

• The steering adjustment intervals are normalized at 80 km/hr. These intervals are 

constantly calculated. Whenever it reaches a threshold value, the driver is 

classified as drowsy. The value of drowsiness threshold is not constant but it 

varies with speed. The driving threshold is calculated by taking the product of the 

mean value of learned steering adjustment intervals in the normal state and the 

mean value of most recent steering adjustment intervals. The results show good 

correlation with EEG. 

Siegmund et al (1996) conducted an experiment based on the performance of 17 long 

haul truck drivers under alert and fatigued conditions on a closed circuit track. They 

presented a steering based set of weighing functions. These functions are based on 

steering angle and steering velocity. According to the researchers, these weighing 

functions are correlated with EEGs and subjective evaluations of drivers. According to 

their findings, phase plots of steering wheel angle verses steering wheel velocity can be 

used as an indicator of drowsiness. 

Sayed and Eskandarian (2001) developed an algorithm, which is based on the ANN 

learning of driver steering. They trained an ANN model using data from a driving 

simulator, driven by human subjects under various levels of sleep deprivation. The 

model identified drowsy and wake steering behavior, calculated over fixed period of 

time, with good accuracy. 

15
Vehicle Speed 

Generally, variability in speed has not shown any significant results that can be used to 

predict drowsiness. Safford and Rockwell (1967) reported no increase in speed 

variability during a 24-hour driving experiment. 

Riemersama et al (1977) recorded vehicle speed during an 8 hour night driving 

experiment. They reported an increase in the standard deviation of speed, calculated over 

45 minute intervals, after the first 3 hours of driving. 

Mackie and O’Hanlon (1977) recorded speed in a 6 hour driving experiment, with a 45 

minute pause after 3 hours of driving. The researchers reported a regular increase in the 

standard deviation of speed from the third driving hour. 

Vehicle Lateral Position 

Several researchers such as Mast et al (1996) found that the lane tracking ability 

decreases as the time on task increases. Skipper et al (1984) found that measures related 

to vehicle lane position could be used to detect drowsiness. Variables such as the number 

of lane deviations, the standard deviation of lane position, and the maximum lane 

deviation are found to be highly correlated with eye closures. According to Dingus et al

(1985), the mean square of lane deviation and mean square of high pass lateral position 

show good potential as drowsiness indicators. 

Stein (1995) studied the effect of impairment on driving performance in truck drivers. 

Using data from a simulator experiment, Stein found that the standard deviation of lane 

position increases remarkably after the driver gets fatigued at 13 hours of driving. The 

standard deviation of the heading error also began to increase after 13 hours. 

Pilutti and Ulsoy (1995) performed experiments on the driving simulator at the Ford 

Research Laboratory for detecting driver fatigue. The results, reported by the authors, 

show that only the standard deviation of lateral position show significant change and 

correspond well with the PERCLOS model. 

Yaw/Brake/Acceleration Activity 

Dingus et al (1985) found that the yaw deviation variance and the mean yaw deviation 

(calculated over a three minute period) show some promise to be considered as 

drowsiness indicators. 

However, several researchers found no relation between drowsiness and vehicle yaw, 

brake, or acceleration. Safford and Rockwell (1967) analyzed data from a twenty-four 

hour driving experiment and reported that the accelerator pedal reversals are correlated 

with driving time; however, according to the literature review conducted by Dingus et al

(1985), there is little evidence of any relation between accelerator activity and time or 

drowsiness. In addition, researchers such as Brown (1966) found no evidence of any 

correlation between accelerator and drowsiness. 

16
Summary of Suitability of Vehicle Performance Output for Drowsiness 

Detection 

The main advantages and disadvantages associated with using performance outputs of the 

vehicle to measure driver drowsiness include: 

• No electrodes and wires are to be attached to the body of the driver. 

• No cameras, monitors, light sources, or other devices are to be aimed at the driver. 

• No dependence on the environmental and other road conditions. 

• Less computational power is required for processing signals such as steering 

angle, which makes the online processing of data easily achievable. 

• Hardware requirement for capturing signal from vehicle components such as 

steering, throttle, and gas paddle are much less than that required for an image 

processing or human body signals. These are often much cheaper and readily 

available. 

• Because of the non-obtrusive nature of these methods, they are more likely to be 

applicable to drivers. 

• Due to variation in the dynamics of different types of vehicles, a universal system 

that will fit all vehicles is very difficult to achieve. These systems must be tuned 

in for the type of vehicle in use. 

• Accuracy may not be very high as compared to EEG monitors, since EEG 

monitors are constantly attached to the body and a signal is received even if the 

car is not in motion. 

17
TRUCK DRIVING SIMULATOR Laboratory 

CISR drowsiness detection models are based on experimental data recorded in a driving 

simulator. Initial experiments were performed in a simulated environment representing a 

passenger car. To gather additional data for this research, a driving simulator capable of 

simulating a truck-driving environment was needed. Three options were investigated to 

arrive at a solution for truck simulations: 

• Modify the existing vehicle simulator, with software from Systems Technology 

Inc. (STI), at CISR to accommodate truck driving simulation 

• Rent or short-term lease a truck simulator at an existing facility or from another 

vendor 

• Purchase a new low cost driving simulator capable of simulating truck driving 

After considering each option, FMCSA and CISR agreed to set up a new low-cost truckdriving simulator. This new simulator is entirely dedicated and specific to truck driving 

simulation tasks. This provides CISR with more flexibility to develop, integrate, and test 

new drowsiness detection and warning systems. In addition, the new technologies and 

state-of-the-art graphics of this new system allow CISR to conduct its studies with a very 

advanced simulation tool. 

To set up this new driving simulator, a partnership was established between CISR and the 

Modeling, Simulation and Driving Simulators (MSIS) research unit of the French 

National Institute for Transport and Safety Research (INRETS). This agreement 

combined INRETS globally acknowledged expertise in the development of driving 

simulation tools with CISR experience in integrating driving simulators. In this project, 

INRETS provided CISR with free access to its simulation software, while CISR was 

responsible for acquiring the truck simulator hardware and integrating the new simulator. 

CISR established the new truck-driving simulator at the GWU campus in Ashburn, VA. 

In a preliminary phase, several driving simulators were visited. Based on the 

performance objectives for the truck simulator, existing alternatives for integrating the 

new simulator were identified. Technical challenges and potential issues involved in 

realistic simulations of driving were investigated to guide the selection of the simulator 

hardware and layout. 

The second phase of this task consisted of integrating the new driving simulator. In the 

following pages, full details of the organization and capabilities of the INRETS 

simulation tools are provided. Work and activities performed to set up the new simulator 

are then extensively described, including design, fabrication, construction, assembling, 

and adaptation tasks. Considerations specific to the simulation of commercial vehicle 

operations and particular equipment required for the study of driver drowsiness are 

reviewed. The method used for creating a driving scenario for the drowsiness detection 

experiments is also presented, and details on the defined scenario are provided. 

18
Prior to conducting the drowsiness experiments, the new truck simulator was calibrated 

and validated. The INRETS vehicle dynamic model was initialized with the vehicle 

parameters of the real truck. The resulting heavy truck driving simulation was then tested 

and rated by professional truck drivers and experts from the driving simulation and 

human factors research fields. 

SIMULATOR REQUIREMENTS 

Review of Existing Simulators / Performance Objectives of the Simulator 

CISR explored several options for a truck driving simulator. Cost, hardware 

requirements, and technical challenges of each solution were evaluated and weighted. 

Three options were investigated to arrive at a solution for truck driving simulations: 

• Modify the existing STI vehicle simulator at CISR to accommodate truck driving 

simulation. 

• Rent or short-term lease a truck simulator at an existing facility or from another 

vendor. 

• Purchase a new low cost driving simulator capable of simulating truck driving. 

Each of the above three options has its own set of advantages and disadvantages. Option 

one, modify the existing STI simulator, has many advantages as CISR already has the 

required hardware and software. However, further investigation found the following 

significant drawbacks: 

• Poor Graphics: Graphic display of simulated driving scenario and other objects 

are of poor quality and cannot be enhanced. Texture, color, and modeling details 

are not up to date and do not provide a realistic virtual environment. 

• Hardware Requirements: It was found that the entire computer and other 

hardware would have to be replaced in order to run the upgraded software. This 

would offset any advantage gained by the fact that CISR already owns the system. 

• Gearbox and Clutch: STI was not able to provide hardware for simulating truck 

gearbox and clutch operation. The gearbox has to be purchased from another 

company that would have created compatibility problems. Writing an interface 

would have made the job more expensive and time consuming. 

• Poor Steering Feel: The feeling of steering aligning torque and road grip in the 

STI system is not very realistic particularly for simulating heavy truck steering. 

The steering system design is more suited for small vehicles and is not compatible 

with a large diameter steering wheel. This would have required the complete 

redesign of the steering system hardware and control algorithm; again, a very 

expensive and time-consuming option. 

19
• Rear Projection for Side View Mirrors: In its current configuration, the STI 

simulation only supports three video channels and can be used for front projection 

only. Two extra channels are required to accommodate side view mirrors, which 

are a very important component of truck driving simulation. 

• Data Acquisition Reliability: The STI system, although capable of acquiring a 

large amount of date with a variety of options, keeps data in the computer 

memory and saves it to the hard drive only after the successful completion of the 

simulation. This delayed storage can cause a loss of data in the event of a 

simulation system or operating system crash. This possibility makes it very 

undesirable for experiments such as fatigue detection, which require very long 

runs and continuous data acquisition. 

The second option of renting or short-term leasing of a truck simulator was also explored 

but was not considered to be a favorable option due to various reasons. The options of 

leasing a simulator that can be moved to the CISR premises were very limited. None of 

the available options have the required capability and demonstrated operational ability to 

do the job. The options of renting a simulator at an existing facility were also considered. 

The major problems with this option were logistical issues particularly for the experiment 

subjects; recruitment of truck drivers from a far off location was a big issue. Cost and 

availability were also an issue. Facilities such as NADS are too expensive and not 

available for a long period of time. 

CISR, in mutual agreement with FMCSA, decided to set up a new low-cost truck driving 

simulator rather than upgrading its current STI car-driving simulator. This new simulator 

is entirely dedicated and specific to truck driving simulation tasks. This option provides 

CISR with more flexibility to develop, integrate and test new drowsiness detection and 

warning systems. In addition, the new technologies and state of the art graphics of this 

system will allow CISR to conduct its studies with an advanced simulation tool. 

A comprehensive review of existing technologies for commercial motor vehicle 

simulation was performed. Experiences of other research centers and organizations from 

both public and private sectors were reviewed. In addition, the research team attended 

various conferences and events related to driving simulation to gain knowledge on 

driving simulators integration and to establish contacts with key players of the driving 

simulation field. Several driving simulators were also visited ranging from state-of-theart installations of multi-million dollar budgets, such as the Ford Virttex and Daimler 

Chrysler driving simulators, to simulators operated by academic transportation research 

institutes, including the University of Michigan GlobalSim DriveSafety simulator and 

PennState STI/SCANeR truck driving simulator. The functional requirements for the 

new CISR truck driving simulator were assessed. 

In concurrence with FMCSA, a partnership was established between CISR and the 

Modeling, Simulation and Driving Simulators (MSIS) research unit of the French 

National Institute for Transport and Safety Research (INRETS) to set up a new 

generation simulator at the GWU campus in Ashburn, VA. 

20
Driving Simulation Challenges 

Driving simulation is a complex task that requires the following four basic components, 

Gruening et al (1998): 

• Simulation of the physics of the vehicle and its interaction with road surface 

• Simulation of the surrounding environment, including other vehicles 

• Integration of informative systems and displays that enable subjects to interpret 

the state of the vehicle model (e.g., sound rendering, instrument panels, motion 

base, etc.) 

• Integration of control devices (e.g., steering wheel, accelerator pedal, brake pedal, 

shift lever, clutch pedal, and other vehicle controls) 

In the generic architecture of a driving simulator, a computing unit calculates the vehicle 

dynamics parameters according to drivers’ inputs. Results for vehicle position, speed, 

acceleration, heading, and traffic information are then sent to the visual display system. 

Commands for engine noise and subsidiary simulation sounds are sent to the sound 

reproduction system. Control feedback signals are sent to a steering torque controller, a 

pedal reverse force system, and cabin instrument panel displays (e.g. the speedometer and 

tachometer). 

The next sections combine results from previous research and experience gained through 

the development of the CISR new truck driving simulator to identify factors that can 

affect driving in a simulated environment. The understanding of the following challenges 

and potential issues is fundamental for the successful design and implementation of a 

driving simulator. 

Visual Cues and the Simulator Display 

Previous research has proven the importance of image refresh rate and time delay on 

driver’s performance in driving simulators (Hogema, 1997; Sudarasan et al, 1997). 

Refresh rates of 60 Hz and higher are usually considered ideal. Visual delay should be 

minimized as much as possible and compensation techniques can be implemented if 

necessary to bring the system response to driver’s inputs closer to real world driving. 

Also, the presence of roadside objects has been shown to improve drivers’ ability to 

estimate their speed and therefore enhance their driving performance (Glaser and Fisher, 

1997; Levine and Mourant, 1996). 

Audio Cues and Simulator Sound 

Sound rendering plays an important role in the immersion feeling of a driving simulator 

subject. Wind and engine noise contribute to fatigue in drivers who have logged many 

hours. Sirens and horns divert their attention from the task at hand and traffic noise 

affect a driver’s state of being and decision-making. In addition, squealing tires are 

indicators that the vehicle is being pushed towards its handling limits. Spatialized sound 

21
generation replicating both vehicle and traffic sounds, and vibrations transmitted to the 

vehicle cabin can significantly enhance the overall realism of a driving simulator. 

Attention should be paid to not provide the driver with an excessively loud driving 

environment. Measures of physiological stress in simulators have proven that excessive 

levels of vehicle noise inside the cab can create discomfort and negative response from 

the drivers (Kim et al, 2000). 

Vestibular Cues and Vehicle Motion 

When handling a vehicle, drivers base their decisions on visual, auditory, and inertial 

stimuli. In some situations, linear and rotational accelerations due to vehicle motion can 

significantly guide the driving strategy. 

Previous experiments have shown that speed is underestimated when based on visual 

cues only, resulting in people driving faster in fixed base driving simulators than in 

moving base driving simulators (Reymond et al, 2001). 

On the other hand, CISR believes that careful analysis is necessary when integrating a 

moving platform in a driving simulator as potential problems may arise. Inappropriate 

motion rendering can degrade the driving impression and contribute to simulator 

sickness. 

Moreover, the decision to include motion generation in a driving simulator has to be 

based on the intended function of the new driving simulator. For example, while vehicle 

motion feedback may be necessary for training applications, it is not essential for driver 

drowsiness studies involving continuous highway driving. 

Driving Stress 

Because subjects are conscious that they can not be exposed to risk as they usually are on 

the real road, smaller levels of stress can be experienced in a driving simulator. This 

should either be taken into account when designing the testing protocol of driving 

simulator experiments or discussed in the experiments results. To mitigate potential 

effects of reduced stress on drivers’ performance in a simulator, countermeasures can be 

envisaged to increase subjects’ stress levels. For example, researchers can refrain from 

telling the subjects about the task to be performed, or researchers can increase the driving 

task workload. 

Simulator Sickness 

Simulator sickness can be induced by a discrepancy or delay between visual and motion 

stimuli perceived by subjects. A conflict between the displayed driving scene and the 

inertial accelerations sensed by the drivers can be experienced both in fixed base driving 

simulators and in simulators equipped with moving platforms where inappropriate motion 

rendering can occur. 

Previous research has investigated the effect of various factors on simulator sickness. 

Low refresh rates of the visual display, large field-of-views (Lin et al, 2002; Lee et al, 

1997), excessive vehicle velocity (Mourant and Thattacherry, 2000), and sharp curves in 

22
the driving scenario (Nilsson, 1993) are factors susceptible to contribute to simulator 

sickness. 

Effects that can potentially contribute to or mitigate simulator sickness have to be 

carefully investigated in the early stages of a driving simulator setup when designing the 

display system and defining the driving scenario. For example, a fixed reference in the 

displayed image (e.g., a fixed background scene), can reduce the motion impression and 

provide subjects with a sense of stability. This simple alternative can help reduce 

simulator sickness. 

Steering Torque Feedback 

In the virtual environment of a driving simulator, a driver controls the vehicle position 

via inputs to the steering wheel. The simulator’s steering system should be capable of 

instructing the driver about the amount of steering correction to apply and transmitting 

the driver’s steering inputs to the vehicle dynamics program during simulation. Since 

steering is the main control device available to the driver in a driving simulator, a driver’s 

performance during an experiment greatly depends on the quality of the steering torque 

feedback. Previous literature has extensively reported the importance of the response of 

steering feedback systems on driver steering activity in driving simulators (Espié et al, 

2003; Godthelp, 1995; Miyamoto et al, 1991; Liu and Chang, 1995; Howe et al, 1997; 

Jang, 1996; Fujioka et al, 1999; Chen and Ulsoy, 2002). 

Driver-Vehicle Performance in Simulator and Driving Simulator Validation 

A driving simulator can only be a valuable research tool if it has been validated for a 

selected driving application. However, no standard method exists for validating a driving 

simulator. 

CISR believes that thorough validation of a driving simulator should include both a static 

evaluation of the simulator components and a subjective assessment (e.g., questionnaires 

filled by professional drivers or simulation experts) of the simulator ability to accurately 

reproduce the task being studied. According to Radwan (1998), the most prominent 

dependent variables used in simulator validation studies are driving speed, lateral 

position, and steering behavior. 

INTEGRATION OF THE TRUCK SIMULATOR 

As mentioned previously, a partnership was established between the CISR and the 

Modeling, Simulation and Driving Simulators (MSIS) research unit of the French 

National Institute for Transport and Safety Research (INRETS). This agreement 

associates INRETS’ recognized expertise in the development of driving simulation tools 

with CISR’s experience in integrating driving simulators. In this project, INRETS 

provided the CISR with free access to its simulation software, while CISR was 

responsible for integrating the new simulator hardware. 

23
INRETS Driving Simulation Environment 

This set of simulation tools consists of several modules organized in a common 

architecture, called ARCHISIM. The modules include:

• The vehicle dynamics model receives inputs from the simulator cabin acquisition 

card and computes the real-time response of the vehicle in a traffic model. The 

vehicle parameters of the INRETS passenger car model were adjusted to simulate 

realistic truck dynamics behavior. The consistency of the modified model for 

driver drowsiness applications was then verified by experts and professional 

commercial vehicle drivers as described later in this report. 

• The traffic model of ARCHISIM is able to simulate realistic driving situations 

involving tens of vehicles in complex traffic environments. Unlike most existing 

traffic simulation tools, the computation of traffic in ARCHISIM is not based on 

car-following algorithms but on a unique method, which evaluates the space 

available in front of a vehicle to determine its behavior in the simulated traffic. 

This approach is believed to better imitate how drivers behave in real life 

situations and, thus, leads to a more realistic replication of traffic. Figure 3.1 

shows the traffic simulation interface of ARCHISIM with detailed information of 

the driven vehicles. The behavior of other vehicles can be determined 

autonomously or programmed to fit a specific experiment’s needs. 

Figure 3.1: Traffic simulation software (Dr2) 

24
• ARCHISIM enables users to define the sub-components of a driving scenario, the 

vehicles, traffic signs, roadside objects, surrounding environment, pre-determined 

traffic events, etc. Actions of the driving scenario can be triggered by the 

operator during simulation or by various pre-defined traffic conditions, for 

example, based on distance traveled, time elapsed, vehicle speed, etc. 

• INRETS haptic steering feedback algorithm is based on models used in robotic 

teleoperation (Espié et al, 2003). In this model, the torque generated on the 

steering shaft is equivalent to the torque generated on the wheel pivot. The 

induced rolling effect is also taken into account in the computation of the steering 

torque feedback. More details of the steering feedback calculation method are 

provided in Appendix B. 

• Spatialized sounds from the vehicle engine and surrounding traffic environment 

are reproduced. Simulated effects include engine sound during vehicle start, 

vehicle ride, up and down shifting of gears, and the sound of passing vehicles. 

The car sound samples provided by INRETS were modified for a more realistic 

truck sound environment. Examples of the changes made to the truck starting 

sound are shown in Figure 3.2. Their frequency was decreased, their amplitude 

was increased, and bass sound was added to the original signals to amplify a 

trembling effect in the cabin equivalent to the normal vibrations in a truck cabin. 

Figure 3.2: INRETS original vehicle start sound and CISR modified truck start 

sound 

Considerations Specific to Commercial Vehicles 

Many aspects of commercial vehicle operation differ from passenger vehicle driving. 

Features specific to truck driving were identified, and particular issues involved in the 

duplication of a truck environment were considered, including: 

• The height of the truck cabin is critical in the limited space of a laboratory since 

high cabin roofs can block the images projected on the front screens. This 

concern guided the selection of the cabin for the new simulator. 

25
• A hood must be placed in front of the truck cabin to replicate the driving view of 

most long-nose heavy trucks commonly found on U.S. roads. 

• A dominant view of the road ahead should be displayed to reproduce the high 

driving position of heavy trucks. 

• A gearbox must be integrated with both automatic driving and manual driving 

modes to simulate the different gear shifting modes found in U.S. trucks. 

• The sound and vibrations transmitted to the vehicle cabin must reproduce the 

noisy and trembling environment of a real truck cabin. 

• Rearview projection should be available in the simulator cabin to allow drivers to 

manage the behavior of their trailer as they usually do on the road. 

Based on these requirements, the necessary equipment was designed and integrated in the 

new simulator as detailed in the next section. 

Hardware 

In a driving simulator, the driver interacts with a simplified driving environment. Due to 

physical limitations of the simulator, the driver perceives fewer parameters during 

simulation than during real world driving. A driving simulator should aim both for 

replication of an embedded and realistic driving environment and for accurate imitation 

of the driving task under study. CISR developed its truck driving simulator for driver 

drowsiness studies according to the organization illustrated in Figure 3.3. 

CISR decided not to include a motion base in its new truck simulator. While moving 

bases can improve the realism of the driving simulation, they are believed to have a 

limited effect in drowsiness detection experiments, which involve highway driving 

characterized by low accelerations. 

In most low-cost driving simulators, the same computer is responsible for data 

acquisition from the vehicle cabin; calculation of vehicle dynamics, traffic, and sound 

models; and generation of force feedback commands for in-vehicle controls. A key point 

in this simulator is the use of a dedicated micro-controller to manage vehicle cabin 

interfacing and steering actuator control processes. This additional interface layer, called 

a Generic Instrumentation Electronic Card (CEGI), receives digital and analog inputs 

from the vehicle cabin sensors, transmits all measured information to the computing unit 

of the vehicle dynamics model, controls in-vehicle indicators and dashboard gauges, and 

sends commands to the steering actuator power module. This second electronic unit then 

uses Pulse Width Modulation (PWM) to generate steering torque signals to be sent to a 

DC motor. 

26
Figure 3.3: A Schematic Diagram of the CISR Truck Driving Simulator 

Truck Cabin and Laboratory Layout 

The first task was to prepare and remodel the laboratory to fulfill all requirements of the 

new driving simulator. The room was entirely painted in black and black shades were 

installed to enhance the driver’s feeling of immersion and improve the contrast of the 

displayed images. Any objects potentially intersecting with projection beams (including 

HVAC components, light fixtures, and sprinkler system accessories) were reinstalled to 

stay clear from the projection path. 

The first design challenge in setting up a new driving simulator is to define the layout of 

the display system and the projectors and screens, around the vehicle cabin in the 

available laboratory space, as shown in Figure 3.4. The available projecting distance, the 

width of the displayed scene viewed by the driver, and the lab dimensions guide the 

selection of vehicle cabin, projectors, and screens, since interference between cabin and 

projected beams should be avoided. A used International 8100 truck cabin was selected 

based on its relatively small size and weight in its class (Figure 3.5). Preparation of the 

cabin and fabrication of a support base are presented in Appendix B. 

27
Projectors 

The choice of a projector depends on the desired field-of-view (FOV). While a larger 

FOV produces better immersion of the driving subjects in the virtual environment, it has 

also been found to contribute to simulator sickness. Compromise values for front 

projection FOV angles generally range between 110° and 160°. 

Both front and rear projections were required for the truck driving simulator. Three 

projectors and screens enable a 135-degree wide front vision. The rear vision is created 

by two projectors and screens, one for each side, which are viewed through the truck side 

mirrors. 

The good contrast ratios, brightness levels, and image sharpness achieved by DLP-based 

projectors make this technology a good and affordable choice for driving simulation 

applications. 

Figure 3.4: General layout of the driving simulator display system 

28
Figure 3.4: General layout of the driving simulator display system (continued) 

Figure 3.5: Driving simulator truck cabin 

Screens 

While curved screens theoretically produce better optic flow rendering, additional 

precautions are necessary to avoid image deformation and achieve correct alignments at 

image junctures. On the other hand, borders between screens potentially visible to the 

drivers are inevitable when using flat screens. An alternative is to place these borders 

behind the vehicle cabin pillars from the driver’s perspective. By not seeing the border of 

each screen, the driver will have the impression of a single continuous image projected in 

front of him. 

29
Consequently, while curved screens represent the best solution for expensive highfidelity driving simulators, flat screens remain a good choice for low to medium fidelity 

simulators. For the fidelity required for these experiments, the research team believe that 

a simple screen design using white mat finish non-reflective display materials can 

achieve acceptable levels of performance. 

The following basic rules must be considered to determine the screens positions (Figure 

3.6): 

• To avoid optical distortions, the driver should sit at equal distances from the 

center of each screen. 

• The distance between the screens and the driver is crucial. Placing the driver too 

close to the screens results in large angles between the screens that compromise 

the driver’s impression of seeing a single continuous image. On the other hand, 

placing the driver too far away from the screens limits the width of the scene 

observed by the driver. Driver’s positioning is commonly considered adequate 

when the driver is placed 9 to 10 feet from the screens. The screens’ orientation 

angles are obtained as a consequence of this requirement. 

For the CISR new truck simulator, the research team adopted direct projection for the 

three front screens and back projection for the two rear screens. 

More details on the design and fabrication of the screens are provided in Appendix B. 

Figure 3.6: Front and rear projection in the truck driving simulator 

Computer Hardware 

Creating complex three-dimensional driving scenes involves highly demanding graphic 

applications that require significant computing power. The performance levels of both 

the processor and graphic cards are crucial for real-time generation of detailed virtual 

environments. 

Seven networked PCs run this truck driving simulator. Five PCs (Intel Pentium 4 

processors at 3.0 GHz with 1024 MB SDRAM) equipped with large memory video cards 

(128 MB ATI Radeon 9800 Pro) perform the highly demanding graphic computation of 

the projected three-dimensional driving scenes. One PC is interfaced with the truck cabin 

30
to compute vehicle dynamics, traffic and sound effects. The last computer manages and 

records signals from the eye tracker and video monitoring systems. 

Steering Feedback System 

Computer controlled DC motors provide one of the best options for steering feel system 

because they offer high output torques and good response time at reasonable costs. Faros 

Inc. of France supplied the steering system hardware. A DC motor produces the 

feedback torque. The CEGI controller card designed by INRETS hosting the steering 

feedback algorithm sends steering commands to the steering power module. This PulseWidth-Modulation driven unit then generates high current signals to control the torque 

motor, as shown in Figure 3.7. Delocalized computing of the steering response ensures 

good update rates of the vehicle response during simulation. 

Figure 3.7: Faros steering feedback actuator and gears, INRETS CEGI control 

unit, and PWM power module 

The torque actuator is connected to the steering shaft through a set of spur gears with a 

7:1 gear ratio. The large gear ratio allows the amplification of torque ratio. To prevent 

the steering wheel from turning endlessly, a mechanical stopping mechanism is provided. 

A steel pin mounted on a gear prevents the steering wheel from turning more than 3.5 

revolutions in each direction. Steering alignment errors can produce undesirable steering 

torque and cause premature wear-out of the steering elements. To compensate for small 

mounting errors, a flexible coupling is used to connect the steering shaft to the DC motor 

shaft. More details on the mounting of the steering torque system are described in 

Appendix B. 

In the new truck simulator, the parameters of the steering torque response system were 

adjusted using subjective evaluations to achieve a realistic steering feel. More details are 

provided below in the section Validation of the Simulator. 

31
Gearbox System 

The resistance of the gearbox system to driver’s control inputs should be representative 

of a real transmission. Faros Inc. supplied the manual stick shift Gear Box system, 

shown in Figure 3.8. The new system is an adaptation of the INRETS gearbox system 

developed for a car simulator. The system is capable of simulating a 10-speed gear ratio 

manual stick shift transmission. A set of levers and springs create the feeling of gear 

shifting. Gear position is determined by a round steel pin locking into one of the six 

cavities machine in a steel plate. A flip switch located in the lever hand knob determines 

higher gear ratios. A set of three potentiometers is used for sensing the current gear 

position. Two potentiometers determine the longitudinal and lateral position of the round 

steel pin while a third potentiometer, located in the flip switch in the lever, determines the 

high speed gear position. The shift lever was modified and a mounting frame was 

fabricated to assemble the gearbox inside the vehicle cabin (see Appendix B). 

Figure 3.8: Faros Gear Box system and final assembly in simulator cabin 

Control Pedals 

To measure the driver’s input to the cabin pedals during simulation, optical encoders 

were mounted on the throttle, brake, and clutch pedals, as shown in Figure 3.9. 

Incremental encoders with push-pull output were acquired. A resolution of 500 pulses 

per revolution was selected to ensure precise measurement of the pedal movements. 

New pedal mechanisms had to be designed and fabricated to reproduce the pedal feel 

originally created by hydraulic systems. Passive spring and lever mechanisms were used 

to generate resistance in the pedals, which is described in detail in Appendix B. The 

installation of the optical encoders was designed to be transparent to the subjects when 

driving. Also, the position of the pedals is identical to the original cabin configuration. 

Cabin Adaptation and Instrument Interfaces 

Vehicle cabin instruments, controls and switches have to be integrated in the driving 

simulator and interfaced with the simulation software to reproduce the normal operation 

of a real vehicle and measure drivers’ inputs during an experiment. 

32
Figure 3.9: Optical encoders mounted on the truck cabin pedals 

Electronic circuits and connectors were fabricated for appropriate interfacing of cabin 

sensors (i.e., optical encoders and potentiometers) and for operation of cabin instruments 

(i.e., cabin displays and dashboard gauges), as shown in Figure 3.10. Special hardware 

was also designed to host all communication and control cards. 

Details on electronic adaptations and interface layers are provided in Appendix B. 

33
Figure 3.10: Cabin instruments wiring, dashboard gauges and instrument panel 

Sound System 

The INRETS sound generation algorithm is capable of generating both vehicle sounds 

(e.g., engine sounds during vehicle start and acceleration/deceleration) and sounds from 

surrounding traffic. The INRETS original engine sound samples were edited and 

modified to better resemble the sound rendering of big diesel truck engines. 

A multi-channel sound system composed of five speakers and a subwoofer was installed 

in the new truck simulator. Each element was strategically placed in the cabin to 

optimize the spatial sound rendering, which is described in more detail in Appendix B. 

Driving Scenario 

Developing the right type of driving environment is crucial to the successful simulation 

of any driving task. Each element of the driving scenario has to be defined by the 

researcher according to the experimental protocol. These elements include definition of 

the roadway, surrounding environment and other static objects, vehicular traffic, all 

events that will occur during the experiment, and the type of data that needs to be 

recorded during the experiment. An improper or poorly defined scenario will only 

produce incorrect and useless data. 

Because driving simulation has many limitations in terms of computing and graphic 

requirements, driving scenarios should be defined based on the unique sequence of tasks 

and events required to simulate the driving situation being studied. 

When studying driver drowsiness, the driving scenario should mainly consist of long and 

monotonous driving sections that are likely to induce driver boredom and fatigue. Based 

on statistical data on the characteristics of accidents involving drivers’ drowsiness, the 

following scenario was developed for the truck simulator: 

• A rural interstate highway with speed limits of 55 to 65 mph (70% of crashes 

occur on this type of highways and within this range of speeds) 

• Early morning and nighttime driving (more than 40% crashes occur between 1 am 

and 7 am) 

• Very little traffic and other activity in the surrounding areas representing a 

monotonous and calm atmosphere 

34
This scenario includes realistic sounds, visual effects, and other pre-defined events 

regarding road traffic and traffic controls. 

CISR decided to develop a driving scenario based on a real interstate rural highway and 

acquired hard copies of plans and profile drawings from the Kansas Department of 

Transportation. These drawings cover a 52-mile section of Interstate 70. Alignment, 

traffic, and other environmental conditions on this highway are very consistent with that 

required for studies of fatigued/drowsy driving. 

In collaboration with INRETS, CISR extracted geometric design information from the 

drawings (i.e., horizontal and vertical curvatures, cross-section data, marking 

information, etc.) and created a three-dimensional roadway environment using the 

OpenGL Performer graphics library from Silicon Graphics. Vehicles, traffic signs, and 

roadside and background objects were then added to the model, as shown in Figure 3.11. 

An exhaustive sequence of driving actions and traffic events was defined to complete the 

driving scenario. More details on the creation of the driving geometric database and 

three-dimensional objects of the driving scenario are provided in Appendix B. 

Figure 3.11: 3D scenes from I-70 driving scenario with environment and traffic 

Equipment for Data Acquisition and Driver Drowsiness Measures 

Additional hardware was required in the truck driving simulator to measure and record 

driver’s behavior and level of alertness/sleepiness during the drowsiness studies. 

35
Video images provide valuable information for studying driver behavior. The truck 

driving simulator is equipped with a digital video monitoring and recording system. 

Infrared cameras are placed in the truck cabin, as shown in Figure 3.12. These cameras 

capture images of the driver’s face, body, hands, and feet, and displayed the driving scene 

as viewed from the inside the cabin, shown in Figure 3.13. A digital multiplexer is used 

to manage all video streams simultaneously. A 500 GB storage unit and a DVD-burner 

are used to store the recorded data. 

Figure 3.12: Infrared cameras for foot and pedal movements, driver’s face and 

hands movements, and digital multiplexer 

Figure 3.13: Infrared images of driver’s body, hands, feet and driving scene 

The simulator lab is also equipped with an eye tracking and measuring system. The 

system measures and records the line of gaze and the pupil diameter of the driver during 

experiment. Figure 3.14 shows a schematic of the Eye-Head Tracking system. 

The beam from a near infrared source illuminates the eye and the optical system focuses 

an image of the eye onto a solid-state video sensor (eye camera). Both the illumination 

beam and the eye image are reflected from a visor. The control unit processes the eye 

36
camera signal to extract the elements of interest (pupil and reflection of the light sourc

on the cornea) and computes both pupil diameter and line of gaze. Pupil and corneal 

reflection outlines and center cross hairs are displayed on the pupil monitor over the 

video image of the eye. Eye line of gaze with respect to the helmet is displayed as a 

cursor or set of cross hairs superimposed on the scene camera video image. Calibra

commands and most other inter

e 

tion 

action with the operator take place through the Eye 

Tracker Interface PC terminal. 

Figure 3.14: Eye tracking system schematic 

Validation of the Simulator 

dating the new simulator 

for driver drowsiness studies based on subjective evaluations. 

model. 

d by a 

behavior, was adequate for this study. 

d 

n 

The last phase of the truck driving simulator setup consisted of adjusting the system 

parameters to achieve realistic truck driving rendering and vali

Adjustment of the Vehicle Dynamic Model and Simulated Traffic 

To get a realistic truck driving rendering in various vehicle controls, especially the 

steering, the vehicle parameters of INRETS passenger car model had to be modified. The

vehicle inertial parameters, the suspensions and tires constants, and the steering and gearshifting characteristics of an International 8100 truck were entered in an INRETS 

Because the experiments to be conducted in the simulator do not involve driving 

situations with severe dynamic vehicle responses, this approach, when supplemente

rigorous verification of the resulting truck 

Testing the Simulator 

After completing the installation of hardware and software, the simulator components ha

to be tested for accuracy, reliability, and authentication of data acquisition. Simulatio

under fully loaded conditions were performed for prolong periods of time to test the 

37
system for reliable performance. Potential hardware weak points were identified and 

replaced by alternative solutions. For example, the gearbox wiring design was modified 

n cause the 

and 

 excessive graphic demand were solved. 

ters 

g angle, steering torque, total vehicle load, maximum braking torque, and gear 

d 

 of 

f the simulator steering, gearbox, and pedals 

s. Additional measures included: 

 real 

unrealistic). The overall average response for the steering 

performance was 1.7. 

dings 

lism ealism 

lism 

l Realism

• Scenario Roadway • Side Mirrors Overall Realism 

to prevent future cable breakage. 

Many problems can also arise during operation of any distributed and/or networked 

system due to network congestion, memory usage, or other conflicts, which ca

entire simulation to crash. The system was tested for all these problems, and 

modifications and reconfigurations were made for continuous system operation. For 

example, anomalies in the computation of traffic vehicle interaction were corrected, 

problems of computational overload due to

Subjective Evaluation 

Evaluation of the simulator was performed based on subjective evaluation by experienced 

personal. Because driving feeling differs between heavy commercial vehicles depending 

on their type, brand and model, size, load, etc., the new simulator had to produce a 

driving feeling representative of an average heavy truck. CISR asked three professional 

truck drivers to drive the simulator, and based on their advice, the following parame

were adjusted for a more universal truck driving feeling: steering ratio, maximum 

steerin

ratio. 

CISR then asked other CMV drivers to subjectively evaluate the CISR truck simulator 

after adjustments to the above parameters were performed. Each of the drivers complete

a practice session and was asked to complete a questionnaire regarding performance

various controls and functions of the simulator. Drivers were asked to evaluate the 

overall realism, responsiveness, and feel o

system

• Sound Intensity • Scenario Surroun

• Sound Direction • Scenario Traffic 

• Sound Overall Rea • Scenario Overall R

• Display Graphics • Cabin Driver Seat 

• Display Size/View • Cabin Overall Interior 

• Display Overall Realism • Cabin Overall Rea

• Instruments Overal • Side Mirrors Size 

• Scenario Vehicles • Side Mirrors View 

Overall drivers’ evaluation averaged 1.8 on a scale of 1 to 5 (1 being very close to

truck and 5 being very 

38
The last task in the truck simulator development was the validation of the simulator for 

the driver’s drowsiness studies. CISR invited a panel of experts from the driving 

simulation and human factors research fields to assess the new laboratory set up for 

drowsiness studies. The new simulator received very positive feedback from the experts. 

Minor recommendations were taken into account and corresponding adjustments were 

implemented in the driving simulator. 

39
3. EXPERIMENT AND DATA COLLECTION 

One of the primary objectives of this project was to conduct driving simulator 

experiments in order to collect data on driver behavior. This data is then used to develop 

detection model for driver fatigue/drowsiness specific to the truck driving environment. 

This chapter describes the experiments conducted, data collected, and statistical analysis 

of the data. 

OVERVIEW OF THE DROWSY DRIVING EXPERIMENT 

The experiment was conducted at the CISR Truck Simulator Laboratory located in the 

George Washington University campus in Ashburn, VA. During this experiment, truck 

drivers drove a truck simulator under various levels of fatigue and sleep deprived 

conditions. Data was recorded for parameters related to driver eye activity and vehicle 

driving performance. Details of the study population, equipment used in the experiment, 

driving scenario, and experimental protocol are provided in the following sections. 

Study Population 

The subject drivers were recruited through advertisements in local newspapers, The 

Trucker magazine, and by soliciting companies involved in freight and trucking business. 

More then 40 drivers responded; 20 of them consented and completed the practice 

session. However, only 14, including two women, were able to complete the actual 

experiment. All of the subjects were truck drivers with a valid Commercial Drivers 

License (CDL). The subjects represented a variety of truck driving professionals such as 

owner operators, company drivers, and freelance part-timers. Driver age ranged from 23 

to 60 years with an average of 42 years, as shown in Figure 4.1. Subjects were screened 

for heavy intake of caffeine, drugs, sleeping disorders, and susceptibility to simulator 

sickness before being allowed to participate in study. Subjects were generally nonsmokers and low caffeine users, and they did not use stimulants including caffeine 

throughout the study. 

Drivers were selected based on a brief telephone interview after which they visited the 

laboratory. Each participant was asked to fill out two questionnaires regarding their 

general health and their susceptibility to simulator sickness. Those who did not fulfill the 

criteria were screened out. Limited demographic information (e.g., age, gender, driving 

experience, health, etc.), was also collected. The subjects were required to adhere to a 

regular sleep routine with at least 8 hours of sleep before the experiment. Prior to any 

driving session, each subject was required to read and sign an informed consent form and 

an audio video release form. Brief personal information was collected. At the end of the 

experiment, subjects were paid $350 for their time and efforts. 

40
Figure 4.1: Drivers’ age 

Subjects 

Driver Age (years)

0

10

20

30

40

50

60

70

123 4 5 6 7 8 9 10 11 12 13 14

Age 

Equipment 

Experiments were conducted in the CISR Truck Driving Simulator Laboratory (TDSL) at 

The George Washington University campus in Ashburn, VA. TDSL is a high fidelity, 

fully interactive, fixed base driving simulator. It is organized around a full-size truck 

cab, with a computer generated image projected onto a 135 degree front screen system. 

A partnership was established between CISR and the Modeling, Simulation and Driving 

Simulators (MSIS) research unit of the French National Institute for Transport and Safety 

Research (INRETS) to develop this simulator. This simulator was built in-house 

specifically for this study. Details of the simulator are provided in the previous section as 

part of TASK 3. A head-mounted eye tracking and measuring system from Applied 

Science Laboratory (ASL) was used to monitor driver eye activity. 

Driving Scenario 

The driving scenario consisted of a continuous 52-mile interstate highway. This scenario 

represents a portion of interstate 70 in the state of Kansas and extends from Junction City 

to Topeka, KS. Alignment, traffic, and other environmental conditions on this highway 

are very consistent with that required for fatigued/drowsy driving. The selection of this 

section of the highway was not based on accident data and no specific accident data was 

available. Rather, it was the availability of design data and the rural driving environment 

that lead to the selection of this section of highway. Details about the development of 

this scenario are provided in the previous section and in Appendix B. 

Experimental Protocol 

The protocol consisted of testing the driving performance of truck drivers under fatigued 

and non-fatigued conditions in a driving simulator. There were a total of three simulator 

driving sessions: a practice session, a morning session, and a night session. 

41
Practice Session 

Drivers who responded and showed interest were contacted and briefly interviewed on 

the phone for preliminary screening. Those who qualified were asked to visit the GWU 

campus in Ashburn, VA for the informed consent process and a practice driving session. 

As required by the Institutional Review Board (IRB), drivers were asked to read an 

inform consent form and, if they agreed, sign the form. They also filled out a 

questionnaire regarding personal information and simulator sickness. Drivers then took a 

tour of the simulator laboratory and were briefed about the experiment and operation of 

the simulator; all the functions and driving controls were explained to them. Each subject 

was given detailed written instructions of what was required of her/him in the 

experiment. They were told to follow all the rules of the road and maintain the instructed 

speed limit. They then drove the simulator for one full length of the scenario and they 

were scheduled for the actual experimental session on the day of their availability. Each 

driver was required to complete two experimental driving sessions, a morning session 

and a night session. Table 4.1 shows the amount of sleep deprivation and time schedule 

for the two experimental sessions. 

Table 4.1: Amount of sleep deprivation 

Morning Session Night Session 

Amount of continuous 

wakefulness

1 – 2 hours 18 – 19 hours 

Time schedule 8:30 am – 9:30 am 1:30 am – 5:00 am 

Morning Session 

Subjects were instructed to have at least 8 hours of sleep the night before their scheduled 

testing date. They reported to the simulation laboratory or were picked up by a 

researcher in the morning, 30 minutes before the start of the experiment. In the morning 

experimental session, subjects drove the simulator for one full length of the scenario (i.e., 

52 miles). During this session, subjects were fresh and experienced no fatigue due to 

sleep deprivation. After completing the morning experimental driving session, they were 

allowed to carry on with their daily life activity. Subjects were asked to have a limited 

amount of caffeine intake and no sleep during that day. They then reported back to the 

laboratory the same night for the night session. 

Night Session 

For the night session, subjects were picked up by the researcher from their home or hotel, 

and they arrived at the laboratory 2 hours before the start of the experiment. Subjects 

stayed at the laboratory till the start of the experiment and were monitored for falling 

asleep. A member of the research team remained with the subjects during this time to 

keep them awake and provide any assistance. The night experimental session started at 

1:30 am and continued until 5:00 am or until the driver was too drowsy/fatigued to 

42
continue driving. Most of the drivers stopped before 5:00 am. The same 52 mile driving 

scenario was continuously repeated. In this session, subjects were sleep deprived and 

were susceptible to fall asleep while driving the simulator. After completing the two 

experimental sessions, subjects were debriefed and asked to fill out a questionnaire 

regarding their general condition and simulator sickness. Subjects were driven home and 

were not allowed to drive since they were sleep deprived. To insure the safety and health 

of the participants, a paramedic was present at the CISR laboratory throughout the 

experimental session. 

DATA COLLECTION 

Three types of data were collected during the experiments: data related to vehicle 

parameters, eye closure data, and video images. An overview of this data is provided in 

this section. 

Vehicle Parameter Data 

The vehicle parameter data included speed, acceleration, vehicle lane position, steering 

angle, braking, and heading angle. This data was recorded at a frequency of 20 Hz from 

sensors through simulation software and stored as an ASCII file. There were no 

problems associated with recording except for one subject whose vehicle parameter data 

was not properly recorded. This subject’s data was not used for data analysis and in the 

model development. 

Eye Closure Data 

Eye closure data was recorded at 60 Hz through equipment by ASL. This system records 

pupil diameter by capturing reflection from pupil (bright pupil). Subjects have to wear a 

headband carrying a camera and a source of infrared light. Before the start of an 

experiment, a researcher adjusts the reflecting glass and camera to get a clear picture of 

the eye in the field of view of the camera. The system then tracks the brightest image in 

the frame, which is reflection from the pupil. The system records a value corresponding 

to pupil diameter but will record a zero value every time it looses track of pupil due to 

eyelid closure or any other reason. There were many problems associated with eye data 

recording due to driver behavior and system shortcomings. They include: 

• Not performing with two types of glasses. (Glasses with shiny frames, since the 

reflection from the frames caused it to track the shiny spots rather than the pupil, 

and glasses with certain types of coated lenses that also caused reflection from the 

lens rather than the pupil.) 

• The movement of drivers’ pupils to extreme positions caused the pupil to partially 

disappear, resulting in the system loosing track of the pupil. This occurred when 

some drivers looked left or right to check their side mirrors. 

43
•	 Misaligning the reflecting mirror, which caused the system to lose track of the 

eye. This was due to some drivers, who scratched their face or rubbed their eyes, 

particularly during night sessions. 

Every time the system loses track of the pupil, it records a zero value as it does when the 

eye is closed. Eye closure data for some subjects was not usable while portions of eye 

data for others had to be discarded. Given the very high volume of recorded data and the 

availability of video images of the driver’s face, discarding erroneous data due to eye 

tracker system malfunctioning was not an issue for developing the drowsiness detection 

model and consequently did not affect the final results of this study. 

Video Images 

As already detailed in Chapter 3 of this report, a digital video recording system was used 

to monitor driver’s behavior inside the vehicle cabin during experiment. Infrared 

cameras recorded the driver’s face, steering wheel and instrument panel including the 

driver hands, driver’s foot position, and roadway scene from inside the cabin. Data was 

stored on a separate high capacity hard drive and was later transferred to DVDs and CDs 

for backup purposes. 

DATA ANALYSIS 

Before a drowsy driver detection system can be developed, the data from the experiments 

is analyzed to identify the potential variables that are correlated with drowsiness. This 

section first reviews the characteristics of drivers’ drowsiness, the manner in which it 

could be identified, and how other factors such as fatigue or monotony can also play a 

role in driving performance degradation. Data collected during experiments on drivers 

and their driving outcomes are then individually analyzed. The objective here is to 

identify variables correlated with driver drowsiness. Such variables can then be used 

either as input for a drowsy driver detection system or to detect drowsiness for the 

evaluation of the performance of such a system. 

Identification of Driver Drowsiness 

In order to assess the effectiveness of a drowsiness detection system, it is essential that 

driver fatigue can be identified. The sleep-wake cycle is a primary factor of driver 

drowsiness, and it was used to design the parameters of the night session in order to 

increase the likelihood of drowsiness. Nonetheless, it is essential that the assessment of a 

drowsiness detection system differentiate between fatigue and monotony, both of which 

can cause driver performance degradation. This experiment differentiated between driver 

fatigue and monotony through real-time observations during the experimental sessions, 

off-line video recording, and eye closure data. 

The Sleep-Wake Cycle 

The sleep-wake cycle informed the design of the experimental protocols, which were 

designed to increase the likelihood of driver fatigue during the night driving sessions. 

44
 
The sleep-wake cycle is regulated by homeostatic and circadian factors (NCSDR/NHTSA 

Expert Panel on Driver Fatigue and Sleepiness, 1998). Homeostatic factors are related to 

the neurobiological need to sleep while the circadian pace maker is an internal body clock 

that governs sleeping patterns. The tendency to sleep during sleep cycle is inevitable and 

intrinsic. Falling into sleep is neurobiological responses of the human brain to sleep 

deprivation. 

According to a survey of truck drivers conducted after the practice driving tests, most of 

the drivers indicated that their “tiredness peak time” varies between 2 am and 4 am. The 

survey outcome initiated a test schedule modification in starting test time from 12:30 am 

to 1:30 am. In another survey after night session tests, some drivers also stated that their 

drowsiness level and dozing off experiences during driving diminished after they passed 

the “tiredness peak time”. In addition, they indicated that their dozing off incidents were 

cyclic and sporadic experiences. 

Differentiating Between Fatigue and Monotony 

As described in the Driving Scenario section in Chapter 3, the simulation scenario was 

intentionally monotonous to increase the likelihood of driver fatigue. Another 

consequence of selecting a monotonous driving scenario is that it can decrease drivers’ 

attention and affect their awareness of the driving situation. Driving performance can, 

therefore, be degraded even for alert drivers. In addition, assuming that drivers’ fatigue 

is related to the time spent at the driving task, fatigue was also considered as one of the 

major sources of driving performance degradation during the long driving periods of the 

night sessions. 

Thus, there are two possible candidates, drowsiness/fatigue and monotony, which could 

be responsible for any driving performance degradation. According to the drivers’ 

responses to the questionnaires handed over after completion of each night session, 75% 

of tested drivers had moderate to severe fatigue symptoms, while 36% of the subjects had 

moderate to severe boredom symptoms after the test. The results suggested that 

monotonous driving had less influence on driving performance than the fatigue and 

drowsiness factors. During data analysis, to correlate the various recorded variables to 

drowsiness, the causes of the driving performance degradation were carefully examined 

through different indicators, such as the video recordings, to make sure that drowsiness 

was the dominant effect. 

Assessing Driver Drowsiness 

The drowsiness of the tested drivers is identified and validated in three ways: 

•	 Real-time observation during testing: The driver behavior, performance, and eye 

closure were observed during testing through live video monitors. All behavioral 

signs indicating a state of drowsiness were observed and recorded. The records 

were especially useful for identifying driver drowsiness when other methods like 

eye monitoring experienced measuring problems (see limitations of the eye 

tracker system in the Eye Closure Data section in this chapter). 

45
 
•	 Off-line video recording: Drowsiness was also observed in video recordings from 

each driver’s face. Driver drowsiness was identified based on subjective 

judgment from drowsy facial attributes and eye closure observation. This method 

was used to confirm PERCLOS data. 

•	 Eye closure measures (PERCLOS): PERCLOS quantifies the percentage of time 

the eye is more than 80% closed. This measure indicates the intervals of time 

when the eyes were closed. To check and confirm the eye closure data and avoid 

corrupted data, the data was validated by other methods, especially by video data. 

Eye Closure Data (PERCLOS) 

PERCLOS, the percentage of time the eye is more than 80% closed, is one of the most 

widely accepted measures in scientific literature for drowsiness detection. It has been 

validated using both EEG data and subjective evaluation. One of the limitations of the 

PERCLOS model is that its prediction is good only when using large time intervals. 

Moreover, PERCLOS does not take into account the variability in human behavior as 

blinking activity can significantly differ between individuals. 

In addition, as discussed in the section on Eye Closure Data in this chapter, the 

technology used to monitor eye motion can present some limitations and generate invalid 

results. In this experiment, some portions of the eye data had to be discarded for some 

drivers because the eye tracker system lost track of the eye pupil during the experiment. 

Figure 4.2 displays PERCLOS 30-minute average for each subject. According to the 

figure: 

•	 Each subject who crashed and/or displayed drowsy behavior based on the video 

data, shows significant difference between the morning and night eye closure data 

plots. 

•	 During all morning sessions, the value of PERCLOS was below 12%. Eye 

closure varies among drivers. Some people tend to have longer eye closure during 

alert driving than during drowsy driving. Therefore, the threshold for 

differentiating between alert and drowsy states for each subject can be based on 

the average of each subject’s eye closure during the morning session. Table 4.2 

shows the PERCLOS average for the subjects with no data recording problems. 

•	 The morning plots did not vary significantly with respect to time. 

•	 PERCLOS 30-minute average curves for night sessions had local maxima and 

minima indicating that eye closure did not steadily increase throughout the night 

session. 

46
 
Table 4.2: Mean PERCLOS for morning sessions 

Subject No Mean PERCLOS 

2 0.06 

8 0.03 

9 0.12 

10 0.07 

14 0.06 

16 0.04 

17 0.01 

19 0.08 

20 0.1 

Average 0.06 

Correlation of Performance Variables with Drowsiness Development 

A statistical analysis of the data collected during the experiment helped to study the 

relationship between the different driver performance variables and the development of 

drowsiness. Variables that are highly correlated could be either appropriate inputs for a 

drowsiness detection system or enable the validation of the system by distinguishing 

between drowsy versus non-drowsy behavior. For example, steering activity at the onset 

of drowsiness would be an appropriate input for the system, if a correlation with 

drowsiness exists. On the other hand, the occurrence of an accident would not be an 

appropriate input, since the purpose of the system is to avoid accidents. If it is correlated 

with drowsiness, though, accidents could be an appropriate criterion to differentiate 

between drowsy and non-drowsy behavior. Table 4.3 shows a list of variables for which 

data was collected during the experiment. The analysis of these variables was completed 

in two phases. 

47
 
PERCLOS 30 min Average 

SUBJ # 2 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

PERCLOS_ Night Morning 

PERCLOS 30 min Average 

SUBJ # 7 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

Time (S) 

PERCLOS_ Night Morning 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 

PERCLOS_ 

PERCLOS 30 min Average 

SUBJ # 8 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

PERCLOS_ Night Morning 

PERCLOS 30 min Average 

SUBJ # 11 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 

Time (S) 

PERCLOS_ Night Morning-ERROR 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 

PERCLOS_ 

PERCLOS 30 min Average 

SUBJ # 10 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 

Time (S) 

PERCLOS_ Night Morning 

PERCLOS 30 min Average 

SUBJ # 13 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

PERCLOS_ Night Morning-ERROR ERROR 

PERCLOS 30 min Average 

SUBJ # 17 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 9800 

Time (S) 

PERCLOS_ Night Morning 

PERCLOS 30 min Average 

SUBJ # 15 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

PERCLOS_ Night Morning-ERROR 

PERCLOS 30 min Average 

SUBJ # 16 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

2791 3791 4791 5791 6791 7791 8791 

Time (S) 

PERCLOS_ Night Morning ERROR Night Data 

ERROR 

Night 

Data 

ERROR IN DATA 

COLLECTION 

Error 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 

PERCLOS_ 

PERCLOS 30 min Average 

SUBJ # 20 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 

Time (S) 

PERCLOS_ Night Morning 

Figure 4.2: PERCLOS 30-minute average for morning and night sessions 

48
 
Table 4.3: List of recorded variables 

VARIABLE DESCRIPTION 

SUBJ# Subject number from 1 to 20 

AGE Age of the subject in years 

GENDER Subject gender (male/female) 

ACC Vehicle acceleration 

ACC_MEAN Mean vehicle acceleration 

SPEED Instantaneous speed 

SPEED_MEAN Mean speed 

SPEED_STDEV Speed standard deviation 

LATERALP Lateral displacement from road centerline 

LATERALD Lateral displacement from center of driving lane 

LATERALD_MEAN Mean absolute lateral displacement (from center of driving 

lane) 

LATERALD_STDEV Absolute lateral displacement standard deviation (from center 

of driving lane) 

HEAD Absolute vehicle heading angle 

HEAD _MEAN Mean vehicle heading angle 

HEAD _STDEV Vehicle heading angle standard deviation 

GAS Percentage of full throttle opening 

GAS_MEAN Mean percentage of full throttle opening 

GAS_STDEV Percentage of full throttle opening standard deviation 

BRAKE_TAP Number of brake pedal taps/application 

T_CROSS Total driving time on or beyond the left/right shoulder or 

center line 

T_CROSS_L Total driving time on or beyond the left shoulder line 

T_CROSS_C Total driving time on or beyond the dashed line between travel 

lanes without using the turning signal 

T_CROSS_R Total driving time on or beyond the right shoulder line 

N_CROSS Total number of left/right shoulder or center line crossings 

N_CROSS_L Total number of left shoulder line crossings 

N_CROSS_C Total number of the dashed line between travel lanes crossings 

without using the turning signal 

N_CROSS_R Total number of the right shoulder line crossings 

TTC Time to collide the leading vehicle 

CRASH_RD_L Number of left run-off-road crashes 

CRASH_RD_R Number of right run-off-road crashes 

CRASH_V Number of crashes with another vehicle or barrier 

STEER Steering wheel angle value 

STEER_E Steering wheel angle instantaneous power 

STEER_P_MEAN Mean steering wheel angle instantaneous power 

STEER_P_STDEV Steering wheel angle instantaneous power standard deviation 

PERCLOS Proportion of time that the eyes are closed in a second 

MDL Lane mostly driven 

49
 
In the first phase, the general trend of each variable was studied by analyzing total mean, 

total standard deviation, 30-minute average, and 30-minute standard deviation graphs. 

Thirty minute averaging was selected to smooth the data series by averaging every 30­

minute data intervals into a data point immediately following the interval. The same 

process was also applied for the standard deviation graphs which depict the standard 

deviation for each 30-minute interval. This procedure makes it easier to spot trends and 

eliminates data volatilities and short time fluctuations. The 30-minute window was 

chosen because it creates smoother curves comparing to other smaller time lengths. Note 

that 30-minute average or standard deviation graphs, starts at 1,800 seconds (30 minutes) 

as no 30-minute base data is available before 30 minutes of simulation. 

In the next phase of the analysis, if the general trend of a variable showed a 

distinguishable behavior change between morning and night sessions, the correlation of 

that variable to driver drowsiness was also examined. The variation of a particular 

variable was studied during selected drowsy periods to establish any correspondence 

between this variable and drowsiness. 

This analysis identifies two variables that are particularly useful for the development of a 

drowsy driver detection system. Steering activity is highly correlated with drowsiness 

and would make an appropriate input for such a system. The occurrence of an accident is 

also highly correlated with drowsiness and is especially useful for the evaluation of the 

ability of a drowsy driver detection system to identify fatigued drivers. 

Age and Gender 

Two out of thirteen drivers who participated in this study were females. Therefore, no 

statistically significant conclusion can be made on the effect of gender on drivers’ 

drowsiness. Since drivers’ ages were not uniformly distributed over age categories, the 

potential effect of age on drowsiness likewise could not be analyzed. 

Speed 

Among 77% of the subjects, mean speed (SPEED_MEAN) for the night session shows a 

slight increase comparing to the morning session while speed standard deviation 

(SPEED_STDEV) indicates no significant trend, as shown in Table 4.4. Instantaneous 

speed (SPEED) 30-minute average graphs for all subjects on Figure 4.3 display neither a 

significant nor a drowsiness-correlated change during night sessions. 

Speed limit signs were posted along the entire road in the simulation scenario, governing 

drivers’ traveling speed strategies. However, in Figure 4.3, a slight increase of mean 

speed was observed during night sessions as compared to morning sessions. This increase 

was not significant to a level that can clearly discriminate between the drowsy and awake 

states. 

The testing sessions were terminated when drivers were too sleepy to continue driving. 

Therefore, the amount of data collected for each subjects was different. Figure 4.3 and 

other figures in the following sections show different time scales as the time driven in the 

simulator differed for each driver tested. 

50
 
Table 4.4: Speed mean and standard deviation for morning and night sessions 

SPEED_MEAN (mph) SPEED_STDEV (mph) 

SUBJ# Morning Night Morning Night 

2 62.0 65.1 4.9 4.0 

7 55.0 59.5 7.6 7.2 

8 70.2 72.5 5.1 5.4 

9 59.5 59.3 5.6 3.8 

10 69.6 76.3 10.1 5.6 

11 67.1 72.5 6.3 7.8 

13 66.0 63.8 4.9 4.3 

14 72.9 73.8 9.8 13.2 

15 62.2 66.4 6.9 5.9 

16 66.2 76.1 7.2 8.5 

17 66.9 70.5 6.9 2.7 

19 66.7 69.3 8.3 8.1 

20 66.2 64.9 5.0 2.6 

Average 65.4 68.5 6.8 6.1 

51
 
90 

90 

Speed 30 min Average Speed 30 min Average Speed 30 min Average 

SUBJ # 2 SUBJ # 7 SUBJ # 8 

90
 90
 90
 

85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

85
 80
 75
 70
 65
 60
 55
 50
 

3446 4446 5446 6446
 

Time (S) 

85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 

SPEED (mph)_ SPEED (mph)_ SPEED (mph)_ 

SPEED (mph)_ SPEED (mph)_ SPEED (mph)_ 

SPEED (mph)_ SPEED (mph)_ SPEED (mph)_ 

SPEED (mph)_ SPEED (mph)_ SPEED (mph)_ Night Morning Night Morning Night Morning 

Speed 30 min Average Speed 30 min Average Speed 30 min Average 

SUBJ # 10 SUBJ # 11 SUBJ # 13 

90
 90
 90
 85
 80
 75
 70
 65
 60
 55
 50
 1800 2800 3800 4800 5800 6800
 Time (S) 85
 80
 75
 70
 65
 60
 55
 50
 1800 2800 3800 4800 5800
 Time (S) 85
 80
 75
 70
 65
 60
 55
 50
 1800 2800 3800 4800 5800 6800 7800 8800
 Time (S) 85
 80
 75
 70
 65
 60
 55
 50
 1800 

Night Morning Night Morning Night 

Morning 

Speed 30 min Average Speed 30 min Average Speed 30 min Average 

SUBJ # 15 SUBJ # 16 SUBJ # 17 

90
 90
 90
 90 85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 3800 4800 5800 6800 7800 8800 9800
 

Time (S) 

85
 80
 75
 70
 65
 60
 55
 50
 

1800 2800 

Night Morning 

Night Morning 

Night Morning 

Speed 30 min Average
 

SUBJ # 20
 

1800
 

Time (S) 

50 55 60 65 70 75 80 85 90 

SPEED (mph)_ Night Morning 

Figure 4.3: Speed 30-minute average for morning and night sessions 

52
 
Gas/Throttle Pedal 

Because of the direct relation between speed and throttle opening, like vehicle speed, 

throttle opening cannot be used as a drowsiness indicator. The mean of throttle opening 

(GAS_MEAN as a percentage of full throttle) is shown in Table 4.5, and 30-minute 

average of throttle opening is shown in Figure 4.4. For 85% of the drivers, both of these 

variables show a slight increase during the night sessions compared to the corresponding 

morning sessions but, like speed, this change did not arrive at a significant level. The 

standard deviation of the throttle opening (GAS_STDEV) showed no specific trends, as 

shown in Table 4.5. For most drivers, the 30-minute average of throttle opening follows 

a similar trend as the 30-minute average of speed because of the direct correlation 

between the gas pedal position and speed value. 

Table 4.5: Throttle opening mean and standard deviation for morning and night 

sessions 

GAS_MEAN (% of full throttle) GAS_STDEV (% of full throttle) 

SUBJ# Morning Night Morning Night 

2 47 48 12 10 

7 33 42 10 19 

8 50 59 9 14 

9 45 52 11 11 

10 57 64 12 11 

11 60 68 14 21 

13 51 47 10 7 

14 63 73 20 28 

15 47 51 14 20 

16 50 65 13 21 

17 58 72 10 15 

19 62 65 19 16 

20 50 48 14 12 

Average 51.8 58 13 15.8 

53
 
Throttle opening 30 min Average 

SUBJ # 2 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

GAS (% of full throttle) Night Morning 

Throttle opening 30 min Average 

SUBJ # 7 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 

Time (S) 

GAS (% of full throttle) Night Morning 

Throttle opening 30 min Average 

SUBJ # 8 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

GAS (% of full throttle) Night Morning 

Th

0 

20 40 60 80 

100 

1800 2800 

GAS (% of full throttle) 

Throttle opening 30 min Average 

SUBJ # 10 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 

Time (S) 

GAS (% of full throttle) Night Morning 

Throttle opening 30 min Average 

SUBJ # 11 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 

Time (S) 

GAS (% of full throttle) Night Morning 

Throttle opening 30 min Average 

SUBJ # 13 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

GAS (% of full throttle) Night Morning 

Th

0 

20 40 60 80 

100 

1800 

GAS (% of full throttle) 

Throttle opening 30 min Average 

SUBJ # 15 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

GAS (% of full throttle) Night Morning 

Throttle opening 30 min Average 

SUBJ # 16 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

GAS (% of full throttle) Night Morning 

Throttle opening 30 min Average 

SUBJ # 17 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 9800 

Time (S) 

GAS (% of full throttle) Night Morning 

Th

0 

20 40 60 80 

100 

1800 2800 

GAS (% of full throttle) 

Throttle opening 30 min Average 

SUBJ # 20 

0 

20 40 60 80 

100 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

GAS (% of full throttle) Night Morning 

Figure 4.4: Throttle opening 30-minute average for morning and night sessions 

54
 
Vehicle Acceleration 

There is no significant change in the vehicle acceleration means (ACC_MEAN), (Table 

4.6), and vehicle acceleration 30-minute averages (Figure 4.5) between morning and 

night sessions. This observation suggests no overall correlation between vehicle 

acceleration and driver drowsiness. Also, vehicle acceleration strongly depends on traffic 

and road geometry implying more challenging issues for removing these external effects 

to study the influence of drowsiness on vehicle acceleration. 

Table 4.6: Vehicle acceleration mean and standard deviation for morning and night 

sessions 

ACC_MEAN 2 ( / ) fts ACC_STDEV 

SUBJ# Morning Night Morning Night 

2 0.03 0.01 0.39 0.26 

7 0.20 0.13 0.39 0.92 

8 0.03 0.10 0.36 0.39 

9 0.03 0.01 0.30 0.23 

10 0.04 0.01 0.04 0.46 

11 0.04 0.04 0.59 0.52 

13 0.03 0.01 0.36 0.23 

14 0.04 0.04 0.95 1.31 

15 0.03 0.01 0.46 0.52 

16 0.03 0.01 0.36 0.75 

17 0.03 0.01 0.36 0.46 

19 0.03 0.02 0.56 0.33 

20 0.03 0.01 0.43 0.36 

Average 0.05 0.03 0.43 0.52 

Braking Tap/Application 

The number of brake pedal applications (BRAKE_TAP) showed no significant variation 

in the alert and sleep deprived driving sessions. This variable had the value of zero for 

most of the time. Therefore, the number of brake applications will not provide useful 

information for a drowsiness detection algorithm. 

Crashes 

Four different variables with respect to crashes were analyzed as possible indicators of 

drowsiness: types of crashes, number of crashes, crash time, and crash point. Since 

crash prevention is the goal of a drowsiness detection/warning program, crashes would 

not be an appropriate input for such a system. However, crashes are useful for our data 

analysis because they can serve as a clear indicator of the onset of drowsiness. 

55
 
___

__

_ 

0 

0.02 0.04 0.06 0.08 0.02 0.04 0.06 0.08 

0.02 0.04 0.06 0.08 

2800 3800 4800 5800 6800 7800 4446 5446 2800 3800 4800 5800 6800 7800 8800 

2800 3800 4800 5800 2800 3800 4800 5800 6800 7800 8800 

2800 3800 4800 5800 6800 7800 8800 2800 3800 4800 5800 6800 7800 8800 2800 3800 4800 5800 6800 7800 8800 9800 2800 

2800 3800 4800 5800 6800 7800 

Acceleration 30 min Average Acceleration 30 min Average Acceleration 30 min Average A

SUBJ # 2 SUBJ # 7 SUBJ # 8 

0.08 0.08 0.08 

2800 

ACC (Ft per square second)_ ACC (Ft per square second) ACC (Ft per square second)

ACC (Ft per square second)_ ACC (Ft per square second)_ ACC (Ft per square second)_ 

ACC (Ft per square second)_ ACC (Ft per square second) ACC (Ft per square second) 0.06 0.04 0.02 0.06 0.04 0.02 0.06 0.04 0.02 0 Night Night Night 

0 0 Morning Morning Morning 6446 1800
 1800
 -0.02 -0.02 Time (S) Time (S) Time (S) 

8800 3446
 -0.02 -0.08 -0.08 1800 -0.04 -0.02 -0.04 -0.04 -0.04 -0.06 -0.06 -0.06 -0.06 -0.08 -0.08 Acceleration 30 min Average Acceleration 30 min Average Acceleration 30 min Average A

SUBJ # 10 SUBJ # 11 SUBJ # 13 

0.08 0.08 0.08 2800 3800 4800 5800 6800 0.06 0.04 0.02 0.06 0.04 0.02 0 0.06 0.04 0.02 Night Morning Night Night 

0 0 0 Morning Morning 

1800
 -0.04
 -0.02 1800
 -0.04
 1800 -0.02 
 -0.04
 -0.02 1800 -0.04 -0.02 -0.06 -0.06 -0.06 -0.06 -0.08 -0.08 -0.08 -0.08 Time (S) Time (S) Time (S) 

Acceleration 30 min Average Acceleration 30 min Average Acceleration 30 min Average A

SUBJ # 15 SUBJ # 16 SUBJ # 17 

0.08 0.08 0.08

ACC (Ft per Square second)_ 0.06 0.04 0.02 0.06 0.04 0.02 0.06 0.04 0.02 Night Night Night 

0 0 0 0 Morning Morning Morning 

1800
 -0.04
 -0.02 -0.02 1800
 -0.04
 1800
 -0.04
 -0.02 1800 -0.04 -0.02 -0.06 -0.06 -0.06 -0.06 

-0.08 -0.08 -0.08 -0.08 

Time (S) Time (S) Time (S) 

Acceleration 30 min Average
 

SUBJ # 20
 

0.08 0.06 0.04 0.02 

Night 0
 

8800
 Morning -0.02 1800
 

-0.04
 -0.06
 -0.08
 

Time (S) 

ACC (Ft per square second) ACC (Ft per square second)_ ACC (Ft per square second)

Figure 4.5: Acceleration 30-minute average for morning and night sessions 

56
 
 

Types of Crashes 

Two types of crashes were defined in this project: run-off-road and collision crashes. 

According to test observations, eye data, and video recordings, drowsiness was the only 

cause of all the crashes during night sessions, which strongly implies the correlation 

between crashes and sleep deprivation. Ninety one percent of the night crashes were the 

result of run-off road incidents. 

Number of crashes 

Table 4.7 displays the number and side of the crashes during the night and morning 

sessions for each subject. As expected, the number of crashes heavily increased during 

the night sessions, especially for the drivers who experienced more doze-offs. There 

were very few drivers who experienced crashes during the morning session. These 

accidents were the result of driver inattention but not drowsiness. 

Table 4.7: Number and type of crashes for morning and night sessions 

Night Morning 

SUBJ# MDL CRASH_RD_L CRASH_RD_R CRASH_V Total CRASH_RD CRASH_V 

2 Right 0 0 0 0 0 0 

7 Right 0 9 1 10 1 0 

8 Left 1 0 0 1 0 0 

9 Right 2 2 1 5 0 0 

10 Left 28 19 2 49 0 0 

11 Right 7 1 0 8 1 0 

13 Right 5 7 2 14 0 0 

14 Left 23 6 5 34 1 0 

15 Right 0 0 0 0 0 0 

16 Right 1 0 0 1 0 0 

17 Right 5 4 1 10 0 0 

19 Left 8 2 1 11 0 0 

20 Right 0 1 0 1 0 0 

Average 6.1 4 1 11.1 0.2 0 

About 1/3 of the drivers who mostly drove on the right lane had more left side run-offroad crashes (CRASH_RD_L). Similarly, the drivers with the preference of left lane 

driving had more left side run-off-road crashes (CRASH_RD_L) than right side run-off 

road crashes (CRASH_RD_R). Overall, more left side run-off-road crashes are 

experienced than right side run-off-road crashes. 

Crash time 

Figures 4.6 and 4.7 illustrate crash times for each subject. It should be noted that the 

design of the simulation scenario allowed the driver to continue driving after a crash 

without interrupting the simulation. According to the video data, after each crash, there 

was a significant increase in the driver’s alertness level. Consequently, stopping the 

driving scenario after a crash might give the driver a chance to recover from drowsiness 

and delay the onset of the next sleep experience. The research team decided not to alert 

57
 
the drowsy drivers during their crashing maneuvers to collect more degraded driving 

performance data. Drivers were warned of their drowsiness by a buzzer installed inside 

the vehicle cabin behind their seat, when they reached the boundary of the threedimensional scene defined in the simulation software. Attempting to drive beyond this 

boundary could lead to computing error and simulation termination. 

As a result, drowsy drivers sometimes crashed several times during one drowsiness event. 

Successive crashes could, therefore, occur in short time intervals. The minimum time 

between consecutive crashes of the same type was 13 seconds (subject 14). As shown by 

the eye and video data, during the intervals when there were concentrated numbers of 

crash incidents, the drivers showed more drowsy behaviors and experienced more dozeoffs. In addition, crash incidents were not uniformly distributed over time meaning 

periods of consecutive crash incidents proceeded by time intervals with no crash. This 

confirms the concept that drowsiness is not a continuous phenomenon and can occur 

sporadically. 

Crash point as an indicator of drowsiness 

Assuming that drowsiness was the cause of the accidents during night sessions, crash 

points can be used as a reliable reference for highlighting the drivers’ drowsiness. 

Therefore, the first data ranges selected for data analysis were the intervals immediately 

before crashes. The performance of the drowsy driver detection system in these intervals 

is a key criterion for the evaluation of the system effectiveness. This analysis is provided 

in the next chapter. 

Drowsiness Effect on Steering Wheel Angle and Vehicle Lateral Position 

Driving can be described as a closed loop system in which the driver performs as a 

controller. During normal driving, the road and traffic information is perceived and 

analyzed by the driver, who sends a corrective command to the controlling devices, 

including the steering wheel, gas pedal and brake pedal, in order to keep the vehicle on 

the desired path and speed. Driving performance degradation due to drowsiness occurs in 

two consecutive phases. In the first phase, the so-called impaired phase, the driver 

decision making system is impaired, and the driver cannot smoothly control the vehicle, 

which in our analogy means that the controller is performing with a large error. The 

result is zigzag driving in which the driver cannot smoothly follow the desired trajectory. 

The effect of this phase is obvious in steering and lateral position data. During zigzag 

driving, vehicle lateral displacement and steering wheel angle standard deviation amounts 

increase with respect to normal driving values. This phenomenon may occur on and off 

before dozing off. In the next phase, the dozing off phase, the closed loop system 

becomes an open loop system causing the vehicle to continue its path without any 

correction as a result of the driver’s dozing off. This can be traced by constant steering 

58
 
Crash Time 

SUBJ #7 

56 556 1056 1556 2056 2556 3056 3556 4056 4556 5056 5556 6056 

Time (S) 

Crash Time 

SUBJ #8 

45 545 1045 1545 2045 2545 3045 3545 4045 4545 5045 5545 6045 6545 7045 7545 8045 8545 9045 9545 

Time (S) 

Crash Times 

SUBJ # 9 

43 543 1043 1543 2043 2543 3043 3543 4043 4543 5043 5543 6043 6543 7043 

Time (S) 

Crash Times 

SUBJ # 10 

62 562 1062 1562 2062 2562 3062 3562 4062 4562 5062 5562 6062 6562 7062 

Time (S) 

Crash Time 

SUBJ # 11 

46 546 1046 1546 2046 2546 3046 3546 4046 4546 5046 5546 6046 6546 

Time (S) 

Crash Time 

SUBJ # 13 

46 546 1046 1546 2046 2546 3046 3546 4046 4546 5046 5546 6046 6546 7046 7546 8046 8546 9046 

Time (S) 

Right side run-off road crash Left side run-off road crash Vehicle to vehicle crash 

Figure 4.6: Crash times for night sessions (Subjects 7-13) 

59
 
41 

Crash Time 

SUBJ # 14 

541 1041 1541 2041 2541 3041 3541 4041 

Time (S) 

46 

Crash Time 

SUBJ # 16 

546 1046 1546 2046 2546 3046 3546 4046 4546 5046 5546 6046 6546 7046 7546 8046 8546 

Time (S) 

99 

Crash Times 

SUBJ # 17 

599

1099

1599

2099 

2599

3099

3599

4099 

4599

5099

5599

6099 

6599

7099

7599 

8099

8599

9099

9599

10099

10599 

Time (S) 

29 

Crash Times 

SUBJ # 19 

529 1029 1529 2029 2529 3029 3529 4029 4529 5029 5529 6029 6529 

Time (S) 

43 

Crash Times 

SUBJ # 20 

543 1043 1543 2043 2543 3043 3543 4043 4543 5043 5543 6043 6543 7043 7543 8043 8543 

Time (S) 

Right side run-off road crash Left side run-off road crash Vehicle to vehicle crash 

Figure 4.7: Crash times for night sessions (Subjects 14-20) 

60
 
2147 2157 2167 2177 

Phase 1 

2187 2197 

Phase 2 

SUBJ #7 

Lateral Position 1 min before crash 

0 

2000 

4000 

6000 

8000 

10000 

12000 

14000 

16000 

18000 

2147 2157 2167 2177 2187 2197 2207 

Time (Second) 

LATERALP (mm 

Phase 1 Phase 2 

CRASH 

SUBJ #7 

Steering angle 1 min before crash 

50 

40 

CRASH 

STEER (Degree)

30
 

20
 

10
 

0
 

-10 

-20 

-30 

Time (Second) 

2207 

Figure 4.8: Vehicle lateral position and steering angle one minute before crash 

values over a short period of time and increasing values in lateral displacement data. 

Most vehicle drifts and run-off road crashes occur during this phase. In some occasions, 

drivers who dozed off were lucky enough to continue driving on a straight path without 

any drift or run-off-road crash. This event is more likely to happen in the simulation 

environment than in real world driving because of the road cross slope, which can make a 

vehicle drift to the side, is not modeled in the simulation. Figure 4.8 displays the 

described signs of driving performance degradation in the lateral position and steering 

data one minute before one of the subject’s first crash. 

61
 
Lateral Displacement 

Lateral displacement is defined as the absolute normal distance between the vehicle 

center of gravity and the current lane’s centerline. As previously described, the drivers 

were allowed to continue driving without any interruption after each crash. This created 

unrealistic values of lateral displacement after run-off-road crashes affecting the data 

analysis results. To avoid that, these values were replaced by the value of vehicle’s lateral 

displacement at the time of the crash.

 The lateral displacement mean and standard deviation values for morning and night 

sessions are listed in Table 4.8. All the subjects demonstrated significantly higher lateral 

displacement means (LATERALD_MEAN) and standard deviations 

(LATERALD_STDEV) for night sessions, which confirms our observations about the 

drowsiness effect on the vehicle’s lateral position. 

Table 4.8: Lateral displacement mean and standard deviation during morning and 

night sessions 

LATERALD_MEAN (ft) LATERALD_STDEV 

SUBJ# Morning Night % 

increase Morning Night % increase 

2 0.8 1.2 35 0.7 1.0 29 

7 1.3 1.9 30 0.9 1.6 42 

8 1.4 1.6 9 1.0 1.1 10 

9 1.1 1.5 29 0.9 1.4 39 

10 1.3 2.4 45 1.0 2.9 66 

11 1.3 2.0 34 1.0 1.5 33 

13 1.1 1.3 20 0.9 1.6 44 

14 1.7 2.8 40 1.1 2.5 56 

15 1.6 1.7 10 0.8 0.8 3 

16 1.3 1.7 24 0.9 1.2 29 

17 0.9 1.5 38 0.9 1.2 21 

19 1.5 1.8 18 1.1 1.6 34 

20 1.0 1.3 25 0.8 1.2 38 

Average 1.2 1.7 29 0.9 1.5 40 

The 30-minute average and 30-minute standard deviation graphs of the lateral 

displacement (Figures 4.9 and 4.10) show higher values for a majority of the night 

sessions compared to the morning sessions. These graphs, therefore, indicate that the 

drivers experienced both higher average deviations and more variability in their lane 

keeping results during night driving than during morning driving. Moreover, lateral 

displacement standard deviation values for night sessions vary while the morning values 

are almost constant for most drivers. This variation suggests that drivers’ ability to 

control the lateral position of their vehicle in the traveling lane degrades during the night 

session. Drowsiness not only increases the average lane deviation but also the variability 

of drivers’ lane keeping performance. 

62
 
____

___

1 

1.5 

2 

2.5 

3 

LATERALD (ft) LATERALD (ft) LATERALD (ft) LATERALD (ft)

Lateral Displacement 30 min Average Lateral Displacement 30 min Average Lateral Displacement 30 min Average Later

SUBJ # 2 SUBJ # 7 SUBJ # 8 

4
 4
 4
 4
 

3.5 3.5 3.5 3.5 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

3446 4446 5446 6446
 

Time (S) 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

1800 2800 

3
 

2.5 

2
 

1.5 

1
 

3
 

2.5 

2
 

1.5 

1
 

3
 

2.5 

2
 

1.5 

1
 

LATERALD (ft) LATERALD (ft) LATERALD (ft)

LATERALD (ft) LATERALD (ft)_ LATERALD (ft)

LATERALD (ft) LATERALD (ft) LATERALD (ft) Night Morning Night Morning Night Morning 

0.5 0.5 0.5 0.5
 0
 0 0 0 Lateral Displacement 30 min Average Lateral Displacement 30 min Average Lateral Displacement 30 min Average Late

SUBJ # 10 SUBJ # 11 SUBJ # 13 

5
 4
 4
 4
 4.5 3.5 3.5 3.5 4
 3
 1800 2800 3800 4800 5800
 Time (S) 1800 2800 3800 4800 5800 6800
 Time (S) 1800 2800 3800 4800 5800 6800 7800 8800
 Time (S) 1800 

3
 3 3.5 3
 2.5 2.5 2.5 Night Night Morning 

Night Morning 2.5 2
 2 2 Morning 2
 1.5 1
 1.5 1.5 1.5 1
 1
 1 0.5 0.5
 0
 0.5 0.5 0 0 0 Lateral Displacement 30 min Average Lateral Displacement 30 min Average Lateral Displacement 30 min Average Late

SUBJ # 15 SUBJ # 16 SUBJ # 17 

4
 4
 4
 4 3.5 3.5 3.5 3.5 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

1800 2800 3800 4800 5800 6800 7800 8800 9800
 

Time (S) 

1800 2800 

3
 

2.5 

2
 

1.5 

1
 

3
 3
 3

2.5 2.5 2.5 Night 

Morning 

Night Morning 

Night Morning 

2
 2
 2

1.5 1.5 1.5 

1
 1
 1

0.5 0.5 0.5
 

0
 

0.5 

0 0 0

Lateral Displacement 30 min Average
 

SUBJ # 20
 

4
 

3.5
 

3
 

2.5 

Night 2
 Morning 1.5
 

1
 

0.5 

0 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

Figure 4.9: Lateral displacement 30-minute average for morning and night sessions 

63
 
_

_

_

_

_

_

_

_

1 2 3 

1 

1.5 

2 

LATERALD (ft) LATERALD (ft) LATERALD (ft) LATERALD (ft)

Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral D

SUBJ # 2 SUBJ # 7 SUBJ # 8 

3
 3
 3
 3
 

2.5 2.5 2.5 2.5 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

3446 4446 5446 6446
 

Time (S) 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

1800 2800 

LATERALD (ft) LATERALD (ft) LATERALD (ft)

LATERALD (ft) LATERALD (ft) LATERALD (ft)

LATERALD (ft) LATERALD (ft) LATERALD (ft) 2
 2
 2
 1.5 1
 Night Morning Night Morning Night Morning 

1.5 1.5 1
 1
 0.5 0.5 0.5 0.5
 0
 0 0 0 Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral D

SUBJ #10 SUBJ # 11 SUBJ # 13 

5
 3
 3
 3
 4.5
 4
 2.5 2.5 2.5 1800 2800 3800 4800 5800
 Time (S) 1800 2800 3800 4800 5800 6800
 Time (S) 1800 2800 3800 4800 5800 6800 7800 8800
 Time (S) 1800 

3.5 2
 2 2 3
 Night Night Morning 

Night Morning 2.5 1.5 1.5 1.5 Morning 2
 1.5
 1
 1
 1
 1 0.5 0.5 0.5 0.5 0 0 0 0 Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral D

SUBJ # 15 SUBJ # 16 SUBJ # 17 

3
 3
 3
 2.5 1.5 0.5 2.5 2
 1.5 1
 2.5 2.5 2
 1.5 1
 2
 1.5 1
 Night Morning Night Morning Night Morning 

0.5 0.5 0.5 

0 0 0 0
 

1800 2800 3800 4800 5800 6800 7800 8800 1800 2800 3800 4800 5800 6800 7800 8800 1800 2800 3800 4800 5800 6800 7800 8800 9800 1800 2800 

Time (S) Time (S) Time (S) 

Lateral Displacement 30 min Standard Deviation
 

SUBJ # 20
 

3
 

2.5 

2
 

Night
 1.5 

Morning 

1
 

0.5 

0 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

Figure 4.10: Lateral displacement 30-minute standard deviation for morning and night ses

64
 
The morning (normal) lateral displacement average varies among the drivers due to 

different individual driving styles. Some people have a higher average lateral 

displacement during their normal driving. Nonetheless, this almost constant lateral 

displacement curve for morning driving is assumed as a baseline for alert or normal 

driving performance. 

Figure 4.11 displays PERCLOS and lateral displacement 30 min average and standard 

deviation for a sample of subjects to help the reader easily compare the graphs. This 

comparison illustrates that there is no direct one to one correlation with the PERCLOS 

data. However, when a subject’s lateral displacement standard deviation curve has an 

increasing trend with respect to the corresponding morning curve, the PERCLOS 30-min 

average graph also displays higher values comparing to its alert baseline. 

To have a better understanding of lateral displacement behavior while drivers are drowsy, 

one minute histograms of the lateral displacement during the 5 minutes before crash is 

illustrated in Figure 4.12 for approximately 10% of the total of crashes (12 crashes). The 

five minute interval before a crash is chosen in order to guarantee the prevalence of a 

driver impaired phase. Similar histograms randomly chosen from five minutes of 

morning driving data are also plotted in Figure 4.12 to show an example of lateral 

displacement variation under normal (alert) conditions. Histograms from morning 

sessions are not meant to be directly compared to histograms from night sessions, but can 

provide insight into normal baseline driving data. 

In each one-minute data window, mean, standard deviation, and median of the steering 

signals are depicted. The x-axis shows lateral displacement bins (intervals). The number 

of lateral displacement values that fall into each bin range is counted and displayed as a 

bar. 

These graphs indicate the following: 

•	 The night session histograms show a higher incidence of large lateral 

displacement compared to the morning sessions. 

•	 The night sessions had higher mean and standard deviation values than the 

morning baseline. 

•	 92% of the analyzed crash data had higher standard deviation values during the 

minute intervals immediately preceding the crash, when compared to other night 

session intervals. 

65
 
__

___

3 1 2 

PERCLOS_ LATERALD (ft) LATERALD (ft)

Lateral Displacement 30 min Average Lateral Displacement 30 min Average Lateral Displacement 30 min Average Lat

SUBJ # 2 SUBJ # 10 SUBJ # 13 

4
 5 4
 4 

4.5 3.5 3.5 3.5 

4
 

PERCLOS_ LATERALD (ft) LATERALD (ft) 3
 3 

PERCLOS_ LATERALD (ft) LATERALD (ft)

PERCLOS_ LATERALD (ft) LATERALD (ft)_ 3.5 2.5 Night 3 2.5 2.5 Night Night 

2
 Morning 2.5 2
 2 2 Morning Morning 1.5 1.5 1.5 1.5 1
 1 1
 1 0.5 0.5 0.5 0.5 0 0 0 0 1800 2800 3800 4800 5800 6800 7800 8800
 1800 2800 3800 4800 5800 6800 1800 2800 3800 4800 5800 6800 7800 8800
 1800 2800 

Time (S) Time (S) Time (S) 

Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral Displacement 30 min Standard Deviation Lateral D

SUBJ # 2 SUBJ #10 SUBJ # 13 

3
 5
 3
 3 4.5 2.5 4 2.5 2.5 0.5 0 0 0 0 1800 2800 

3.5 2
 2 Night 3 Night Night 

1.5 Morning 2.5 1.5 1.5 2 Morning Morning 1.5
 1
 1
 1 0.5 0.5 0.5 1800 2800 3800 4800 5800 6800 7800 8800
 1800 2800 3800 4800 5800 6800 1800 2800 3800 4800 5800 6800 7800 8800
 Time (S) Time (S) Time (S) 

PERCLOS 30 min Average PERCLOS 30 min Average PERCLOS 30 min Average 

SUBJ # 2 SUBJ # 10 SUBJ # 13 

0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.6 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

1800 2800 3800 4800 5800 6800
 

Time (S) 

ERROR 

1800 2800 3800 4800 5800 6800 7800 8800
 

Time (S) 

ERROR 

Night 

Data 

0.5 0.5 0.5 

0.4 0.3 0.2 

0.5 0.4 0.4 Night 0.4 Night 

Morning 

Night Morning-ERROR 

0.3 Morning 0.3 0.3 

0.2 0.2 0.2 0.1 0.1 0.1 0.1 

0 0 0 0
 

1800 2800 

Figure 4.11: Comparison of lateral displacement graphs and PERCLOS 

66
 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.7 Median=0.8 Night Mean=0.9 StDev=0.6 Median=0.8 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.1 StDev=0.8 Median=0.9 Night Mean=2.3 StDev=1.3 Median=2.0 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.9 Median=0.5 Night Mean=2.0 StDev=1.1 Median=2.1 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.3 StDev=0.8 Median=1.5 Night Mean=2.2 StDev=0.9 Median=2.0 

0 5 

115 225 3

Counts _ 

SUBJECT # 8 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.8 StDev=0.4 Median=0.9 Night Mean=1.2 StDev=1.0 Median=1.0 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.4 StDev=0.8 Median=1.3 Night Mean=1.6 StDev=1.3 Median=1.3 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.9 StDev=1.0 Median=1.8 Night Mean=2.1 StDev=1.0 Median=2.1 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.7 StDev=0.7 Median=1.6 Night Mean=2.0 StDev=1.4 Median=1.8 

0 5 

115 225 335 4

Counts _ 

SUBJECT # 9 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.6 Median=0.9 Night Mean=2.0 StDev=1.4 Median=1.7 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.7 StDev=0.5 Median=0.6 Night Mean=2.1 StDev=1.8 Median=1.7 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.0 StDev=0.8 Median=0.7 Night Mean=1.9 StDev=1.6 Median=1.6 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.7 Median=0.9 Night Mean=1.9 StDev=1.5 Median=1.7 

0 5 

10 15 20 25 30 35 40 

Counts _ 

SUBJECT # 7 

Figure 4.12: Lateral displacement signal and lateral displacement power 1 to 5 minutes before a crash compar

morning sessions 

67
 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 10
 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.1 StDev=0.7 Median=1.4 Night Mean=1.4 StDev=1.1 Median=1.2 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.9 StDev=0.6 Median=0.9 Night Mean=1.5 StDev=1.5 Median=1.0 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.6 StDev=0.7 Median=1.7 Night Mean=1.7 StDev=1.3 Median=1.3 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.4 StDev=0.7 Median=1.6 Night Mean=1.6 StDev=1.5 Median=1.0 

0 5 

115 225 3

Counts _ 

SUBJECT # 10
 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.4 StDev=1.1 Median=1.3 Night Mean=2.0 StDev=1.6 Median=1.4 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.2 StDev=0.7 Median=1.2 Night Mean=1.0 StDev=1.1 Median=0.6 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.7 StDev=0.8 Median=1.6 Night Mean=1.2 StDev=0.9 Median=1.1 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.4 StDev=0.5 Median=1.4 Night Mean=1.4 StDev=1.11 Median=1.2 

0 5 

115 225 3

Counts _ 

SUBJECT # 11
 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.6 Median=0.8 Night Mean=2.5 StDev=0.9 Median=2.3 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.8 StDev=0.4 Median=0.7 Night Mean=2.0 StDev=1.1 Median=2.0 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.8 StDev=0.6 Median=0.8 Night Mean=1.7 StDev=1.2 Median=1.7 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.5 Median=0.9 Night Mean=2.2 StDev=1.6 Median=2.1 

0 5 

115 225 335 445 

Counts _ 

Figure 4.12 (continued) 

68
 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 13
 

0 

123456

Counts_ 

Lateral Displacement Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.6 StDev=0.4 Median=0.5 Night Mean=1.3 StDev=1.1 Median=1.1 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.6 Median=0.8 Night Mean=1.4 StDev=1.3 Median=1.0 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.6 StDev=0.4 Median=0.6 Night Mean=1.2 StDev=0.9 Median=1.0 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.7 StDev=0.5 Median=0.6 Night Mean=0.6 StDev=0.4 Median=0.9 

SUBJECT # 14
 

0 5 

115 225 335 445 5

Counts_ 

Lateral Displacement Histogram 

2-1 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=2.3 StDev=1.1 Median=2.5 Night Mean=2.9 StDev=1.6 Median=3.0 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=2.2 StDev=0.6 Median=2.3 Night Mean=2.1 StDev=1.3 Median=1.8 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.0 StDev=1.0 Median=0.8 Night Mean=3.3 StDev=1.5 Median=3.3 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.6 Median=0.6 Night Mean=2.2 StDev=1.3 Median=2.2 

SUBJECT # 15
 

0 5 

115 225 335 445 5

Counts_ 

Lateral Displacement Histogram 

2-1 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=2.3 StDev=1.1 Median=2.5 Night Mean=2.9 StDev=1.6 Median=3.0 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=2.2 StDev=0.6 Median=2.3 Night Mean=2.1 StDev=1.3 Median=1.8 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.0 StDev=1.0 Median=0.8 Night Mean=3.3 StDev=1.5 Median=3.3 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=0.6 Median=0.6 Night Mean=2.2 StDev=1.3 Median=2.2 

Figure 4.12 (continued) 

69 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 16
 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.1 StDev=0.7 Median=1.1 Night Mean=1.9 StDev=1.5 Median=1.7 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.1 StDev=0.5 Median=1.1 Night Mean=1.9 StDev=1.0 Median=1.7 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.3 StDev=0.7 Median=1.4 Night Mean=1.8 StDev=1.2 Median=1.6 

Lateral Displacement Histogram 

2-1 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.7 StDev=0.6 Median=1.6 Night Mean=2.0 StDev=1.3 Median=1.9 

Counts 

SUBJECT # 17
 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.6 StDev=0.3 Median=0.7 Night Mean=2.2 StDev=1.2 Median=2.4 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.6 StDev=0.5 Median=0.5 Night Mean=2.1 StDev=1.5 Median=1.8 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.0 StDev=0.5 Median=1.0 Night Mean=1.6 StDev=1.1 Median=1.6 

Lateral Displacement Histogram 

2-1 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=0.6 StDev=0.5 Median=0.5 Night Mean=2.2 StDev=1.3 Median=1.9 

Counts 

SUBJECT # 19
 

Lateral Displacement Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.0 StDev=0.8 Median=0.8 Night Mean=1.1 StDev=1.0 Median=0.9 

Lateral Displacement Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0 1 2 3 4 5 6 7 8 9 10 1112 13 1415More 

Displacement Bins (ft) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=1.6 StDev=1.1 Median=1.5 Night Mean=1.8 StDev=0.9 Median=1.7 

Lateral Displacement Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=1.4 StDev=0.8 Median=1.3 Night Mean=2.1 StDev=1.2 Median=1.6 

Lateral Displacement Histogram 

2-1 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

0123456789

10

11

12

13

14

15

More 

Displacement Bins (ft) 

Counts_ Night Morning 

Morning Mean=2.0 StDev=1.1 Median=1.9 Night Mean=2.4 StDev=1.5 Median=2.2 

Counts

Figure 4.12 (continued) 

70
 
The results show that lateral displacement variability is related to the amount of 

drowsiness in the subjects suggesting that it can be used in as a good measure in a drowsy 

driver detection system. 

Another way of examining a driver’s performance and responsiveness is to study the 

number of marking line crossings (left, center, and right lines) and the total time that the 

vehicle was driven on or beyond the marking lines. The simulated roadway was a four 

lane, divided highway with continuous left and right shoulder marking lines and dashed 

center line. Line crossing occurs when one of the vehicle tires touches the shoulder line. 

The number of center line crossings is defined as the number of times that the vehicle 

crossed the dashed line without using the turning signals. Table 4.9 displays the values 

for left, center, and right line crossings, as well as the lane mostly driven by each subject. 

All the drivers who drove mostly on the left lane had more left line crossings than right 

line crossings. For right lane drivers, left and right line crossings were more balanced, 

with approximately the same number of left line crossings (45%) than right line crossings 

(55%). Most of left line crossings occurred when passing a vehicle on the left. 

Number of Lane Changes 

Figure 4.13 displays the number of lane change (N_LANE_CH) per 30 minutes during 

morning and night sessions for different subjects. Generally, N_LANE_CH values 

increase over the periods that the drivers feel drowsy. This variable shows good 

correlation with the sleep deprivation. However, the variability of this parameter is not 

significant for short time intervals. Therefore, it cannot be used as an indicator in the 

drowsiness detection system. 

71
 
Table 4.9: Time the vehicle was driven beyond marking lines and number of line crossings during mo

sessions (bold values represent night sessions) 

TOTAL DRIVING TIME ON OR BEYOND MARKING 

LINE(Second) TOTAL NUMBER OF CROSSINGS 

SUBJ# T_CROSS_L T_CROSS_C T_CROSS_R TOTAL N_CROSS_L N_CROSS_C N_CROSS_R 

2 36 / 142 57 / 770 18 / 268 111 / 1180 3 / 22 5 / 63 2 / 44 

7 0 / 1 19 / 133 630 / 2010 649 / 2144 0 / 1 6 / 29 7 / 31 

8 367 / 1082 198 / 1384 7 / 51 572 / 2517 8 / 49 9 / 62 2 / 15 

9 45 / 195 117 / 551 141 / 858 303 / 1607 3 / 16 16 / 89 16 / 84 

10 141 / 966 227 / 884 8 / 622 376 / 2472 14 / 75 16 / 110 3 / 40 

11 294 / 1089 187 / 1296 0 / 108 481 / 2493 6 / 44 7 / 57 0 / 13 

13 36 / 150 231 / 788 31 / 425 298 / 1363 5 / 26 10 / 70 5 / 41 14 478 / 1211 208 / 659 1 / 211 687 / 2081 17 /52 19 / 74 1 / 23 

15 185 / 1047 449 / 1369 0 / 4 634 / 2420 6 / 33 7 / 35 0 / 1 

16 87 / 886 281 / 1672 2 / 28 370 / 2586 6 / 73 8 / 81 1 / 7 

17 221 / 1464 15 / 511 1 / 270 237 / 2245 5 / 49 5 / 72 1 / 40 

19 244 / 988 342 / 1053 18 / 119 604 / 2160 11 / 49 16 / 69 5 / 26 

20 70 / 123 27 / 770 2 / 298 99 / 1191 6 / 22 7 / 67 1 / 46 

Average 169.5 / 718.8 181.4 / 910.8 66.1 / 405.5 417 / 2035 6.9 / 39.3 10.1 / 67.5 3.4 / 31.6 

72
 
Number of Lane Change per 30 Min 

SUBJ # 2 

0 

10 20 30 40 50 60 

1 2 3 4 5 

Time Interval (30 min) 

N_LANE_CH_ 

M N 

Number of Lane Change per 30 Min 

SUBJ #7 

0 

10 20 30 40 50 60 

1 2 3 4 

Time Interval (30 min) 

N_LANE_CH_ 

M N 

Num

0 

10 20 30 40 50 60 

1 

N_LANE_CH_ 

M 

Number of Lane Change per 30 Min 

SUBJ #8 

0 

10 20 30 40 50 60 

1 2 3 4 5 6 

Time Interval (30 min) 

N_LANE_CH_ 

M N 

Number of Lane Change per 30 Min 

SUBJ #10 

0 

10 20 30 40 50 60 

1 2 3 4 5 

Time Interval (30 min) 

N_LANE_CH_ 

M N 

Number of Lane Change per 30 Min 

SUBJ #11 

0 

10 20 30 40 50 60 

1 2 3 4 

Time Interval (30 min) 

N_LANE_CH_ 

M N 

Number of Lane Change per 30 Min 

SUBJ #13 

0 

10 20 30 40 50 60 

1 2 3 4 5 6 

Time Interval (30 min) 

N_LANE_CH_ 

M N 

Num

0 

10 20 30 40 50 60 

1 

N_LANE_CH_ 

M 

Number of Lane Change per 30 Min 

SUBJ #15 

0 

10 20 30 40 50 60 

1 2 3 4 5 6 

Time Interval (30 min) 

N_LANE_CH_ 

M N

Number of Lane Change per 30 Min 

SUBJ #17 

0 

10 20 30 40 50 60 

1 2 3 4 5 6 

Time Interval (30 min) 

N_LANE_CH_ 

M N

Num

0 

10 20 30 40 50 60 

1 

N_LANE_CH_ 

M 

Number of Lane Change per 30 Min 

SUBJ #16 

0 

10 20 30 40 50 60 

1 2 3 4 5 

Time Interval (30 min) 

N_LANE_CH_ 

M N

Number of Lane Change per 30 Min 

SUBJ #20 

0 

10 20 30 40 50 60 

1 2 3 4 5 

Time Interval (30 min) 

N_LANE_CH_ 

Figure 4.13: Number of lane change per 30 minutes during Morning (M) and Night (N) sessions fo

73
 
Heading Angle 

Table 4.10 shows vehicle heading angle mean (HEADING_MEAN) and standard 

deviation (HEAD_STDEV) values for each subject during the night and morning 

sessions. Figure 4.14 also displays heading angle (HEAD) 30-minute average graphs. 

Given that lateral position is related to the vehicle heading angle, the conclusions made 

for the lateral position are also applicable to the heading angle. 

Table 4.10: Vehicle heading angle mean and standard deviation for morning and 

night sessions 

HEAD_MEAN (Degree) HEAD_STDEV 

SUBJ# Morning Night Morning Night 

2 9 11 10 14 

7 18 21 34 27 

8 9 11 11 14 

9 13 19 13 24 

10 11 19 16 46 

11 7 14 11 19 

13 13 18 14 29 

14 13 26 18 26 

15 7 9 10 13 

16 9 14 11 19 

17 8 17 11 19 

19 11 15 13 21 

20 9 15 10 17 

Average 10 16 14 22 

74
 
Absolute of Heading Angle 30 min Average 

SUBJ # 2 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absolute of Heading Angle 30 min Average 

SUBJ # 7 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absolute of Heading Angle 30 min Average 

SUBJ # 8 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absol

0 5 

10 15 20 25 30 

1800 2800 

HEAD (Degree)_ 

Absolute of Heading Angle 30 min Average 

SUBJ # 10 

0 

10 20 30 40 50 60 

1800 2800 3800 4800 5800 6800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absolute of Heading Angle 30 min Average 

SUBJ # 11 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absolute of Heading Angle 30 min Average 

SUBJ # 13 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absol

0 5 

10 15 20 25 30 

1800 

HEAD (Degree)_ 

Absolute of Heading Angle 30 min Average 

SUBJ # 15 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absolute of Heading Angle 30 min Average 

SUBJ # 16 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absolute of Heading Angle 30 min Average 

SUBJ # 17 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 9800 

Time (S) 

HEAD (Degree)_ Night Morning 

Absol

0 5 

10 15 20 25 30 

1800 2800 

HEAD (Degree)_ 

Absolute of Heading Angle 30 min Average 

SUBJ # 20 

0 5 

10 15 20 25 30 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

HEAD (Degree)_ Night Morning 

Figure 4.14: Vehicle heading angle 30-minute average for morning and night sessions 

75
 
Steering Wheel Angle 

Since the steering angle data contains positive and negative values, its mean and standard 

deviation does not demonstrate any trend. Thus, the steering angle instantaneous power, 

defined as the steering angle squared, is used to analyze the effect of drowsiness on the 

steering performance. Using steering signal power for analysis provides two advantages 

over using the signal absolute value. First, it represents a meaningful physical 

characteristic of the steering angle signal. Second, it amplifies the large steering 

corrections associated with driver drowsiness and, therefore, increases the difference 

between alert and drowsy steering results. 

In curved sections of the roadway, drivers’ steering wheel inputs do not only correct for 

vehicle lateral deviations but also for the road curvature. The portion of steering angle 

intended to compensate for road curvature was subtracted from the original steering angle 

data. The process of eliminating road curvature is discussed in the section on Effect of 

Road Curvature in Chapter 5. Table 4.11 shows steering angle power mean 

(STEER_P_MEAN) and standard deviation (STEER_P_STDEV) for each subject during 

the morning and night sessions. This table shows that during the night sessions, the 

steering angle power mean and standard deviation have higher values when compared to 

morning values. 

Table 4.11: Steering angle power mean and standard deviation for morning and 

night sessions 

STEER_P_MEAN (Degree2) STEER_P _STDEV (Degree2) 

SUBJ# Morning Night % 

increase Morning Night % increase 

2 6.4 9.1 30 25.0 30.1 20 

7 22.5 55.9 60 191.0 179.8 -10* 

8 4.9 8.6 40 24.4 43.0 40 

9 6.5 20.8 70 20.1 91.4 80 

10 7.3 41.2 80 26.5 152.1 80 

11 3.3 18.0 80 15.9 67.5 80 

13 7.6 14.9 50 27.6 61.1 50 

14 5.8 26.6 80 21.2 97.7 80 

15 4.1 6.2 30 12.2 18.8 40 

16 5.3 19.4 70 20.2 63.6 70 

17 5.7 31.2 80 19.8 66.8 70 

19 8.0 14.5 40 35.1 91.0 60 

20 4.9 13.5 60 21.1 51.2 60 

Average 7.1 21.5 60 35.4 78.0 60 

Figures 4.15 and 4.16 display 30-minute average and 30-minute standard deviation 

graphs. Night curves illustrate increasing trends during the session while morning curves 

show almost constant trends during experiment. From this evidence alone, it could be 

concluded that sleep deprivation affected the steering performance. Moreover, according 

to the PERCLOS graphs (Figure 4.2) and video data, throughout those periods when the 

steering angle power mean and standard deviation showed an increasing trend, the drivers 

76
 
Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 2 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 7 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 8 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

10 20 30 40 50 60 70 80 90 

1800 2800 

STEER_P (Degree)_ 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 10 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 11 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 13 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

10 20 30 40 50 60 70 80 90 

1800 

STEER_P (Degree)_ 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 15 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 16 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 17 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 8800 9800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

10 20 30 40 50 60 70 80 90 

1800 2800 

STEER_P (Degree)_ 

Steering Wheel AngleInstantaneous Power 30-min 

Average 

SUBJ # 20 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Figure 4.15: Steering wheel angle 30-minute average for morning and night sessions 

77
 
Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 2 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 6800 7800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 7 

0 

50 

100 150 200 250 

1800 2800 3800 4800 5800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 8 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

20 40 60 80 

100 120 140 160 

1800 2800 

STEER_P (Degree)_ 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 10 

0 

50 

100 150 200 

1800 2800 3800 4800 5800 6800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 11 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 13 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 6800 7800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

20 40 60 80 

100 120 140 160 

1800 

STEER_P (Degree)_ 

Steering Wheel Angle Instantaneous Power30-min 

Standard Deviation 

SUBJ # 15 

0 

20 40 60 80 

100 120 140 160 

1800 2800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power30-min 

Standard Deviation 

SUBJ # 16 

0 

20 40 60 80 

100 120 140 160 

1800 2800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 17 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 6800 7800 8800 9800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

20 40 60 80 

100 120 140 160 

1800 2800 

STEER_P (Degree)_ 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 20 

0 

10 20 30 40 50 60 70 80 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Figure 4.16: Steering wheel angle 30-minute standard deviation for morning and night ses

78
 
had more prevalence of drowsiness, which confirms the correlation between the steering 

angle power and drowsiness. Figure 4.17 shows the steering angle 30 min average and 

standard deviation as well as PERCLOS data for several subjects at the same time. The 

graphs show a good correlation between the steering and PERCLOS data. 

As previously explained, during the impaired phase, the drowsy driver is less sensitive to 

small corrections and the number of micro-steering wheel adjustments decreases. As a 

result, the steering angle signal oscillates with large amplitudes and thus has a high power 

content. Figure 4.18 shows one-minute steering angle power histograms 5 minutes 

before 12 different crash points, a sample of approximately 10% of the total crashes. A 

five-minute time interval before a crash time is chosen in order to guarantee a prevailing 

driver impaired phase. One-minute steering power histograms randomly chosen from 5 

minutes of morning driving data are also plotted in Figure 4.18 to show an example of 

normal (alert) steering behavior. Since the five-minute intervals selected for both series 

of data do not correspond to similar driving situations, a direct comparison of both 

morning and night histograms is not possible. Morning histograms only provide an 

indication of normal variation of steering angle power, to better assess specific trends in 

the night data. 

In each graph, steering angle power mean, standard deviation, and median are also 

depicted. In contrast with morning figures, night session histograms show that the 

steering signals reached higher power values, suggesting the presence of larger steering 

corrections during the night session. Also, the mean of the steering signal power 

increases for night driving as compared to morning driving. This indicates that not only 

were the macro-steering corrections larger during night driving, but the average steering 

correction was also higher. In addition, during last minute driving before crash (minutes 

1-0), only 50% of the steering data had higher mean and standard deviation values 

comparing to the other four windows (minutes 5-1), indicating the presence of phase 2. 

The occurrence of high amplitude steering has a random nature; a period of steering 

macro-movements can be followed by a micro-adjustment interval. In some occasions, 

the alert drivers’ steering signal histograms may contain high power bins because of 

drivers’ inattention that leads to large steering adjustments. Moreover, throughout the 

night sessions, there were few one-minute window histograms comprising only low 

power bins, which substantiate the spontaneous nature of impaired phase phenomenon. 

On the other hand, during the short dozing off periods, the steering angle signal suddenly 

flattens with almost constant values. This phenomenon is the result of the described open 

loop system in which the dozed off driver is not sending any correcting feedback to the 

steering system. Figure 4.19 highlights this incident for a sample of approximately 10% 

of the total number of crashes (12 crashes). The dozing-off periods were confirmed by 

the eye and video data. As shown in the figure, immediately before each crash point, the 

signal shape is flat. Meanwhile, there were some other steering constant value intervals 

following a sudden steering move, so called ‘drift and jerk’. Drift and jerk phenomenon 

is the result of a quick recovery from a doze-off to avoid a crash. As described before, on 

some occasions during the simulation, the driver was fortunate to keep the steering wheel 

in a correct position keeping the vehicle on the right path during drowsy driving. 

79
 
Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 2 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 10 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Average 

SUBJ # 13 

0 

10 20 30 40 50 60 70 80 90 

1800 2800 3800 4800 5800 6800 7800 

Time (second) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

10 20 30 40 50 60 70 80 90 

1800 2800 

STEER_P (Degree)_ 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 2 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 6800 7800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 10 

0 

50 

100 150 200 

1800 2800 3800 4800 5800 6800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering Wheel Angle Instantaneous Power 30-min 

Standard Deviation 

SUBJ # 13 

0 

20 40 60 80 

100 120 140 160 

1800 2800 3800 4800 5800 6800 7800 

Time (S) 

STEER_P (Degree)_ Morning Night 

Steering W

0 

20 40 60 80 

100 120 140 160 

1800 2800 

STEER_P (Degree)_ 

PERCLOS 30 min Average 

SUBJ # 2 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

PERCLOS_ Night Morning 

PERCLOS 30 min Average 

SUBJ # 10 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 

Time (S) 

PERCLOS_ Night Morning 

PERCLOS 30 min Average 

SUBJ # 13 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 3800 4800 5800 6800 7800 8800 

Time (S) 

PERCLOS_ Night Morning-ERROR ERROR 

0 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 

1800 2800 

PERCLOS_ 

ERROR 

Night 

Data 

Figure 4.17: Comparison of steering wheel angle graphs and PERCLOS 

80
 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 7 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

10 60 110 160 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=26.3 StDev=47.0 Night Mean=14.3 StDev=26.3 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=10.6 StDev=17.1 Night Mean=62.1 StDev=167.6 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=8.3 StDev=11.5 Night Mean=26.5 StDev=53.3 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=16.4 StDev=23.3 Night Mean=67.0 StDev=103.6 

SUBJECT # 8 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

10 60 110 160 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=7.4 StDev=15.3 Night Mean=6.6 StDev=9.9 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 5 

10 15 20 25 30 35 40 45 50 

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.8 StDev=1.6 Night Mean=10.5 StDev=22 

Steering Wheel Angle Instantaneous Power Histogram

 3-2 minute before crash 

0 

10 20 30 40 50 60 70 

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=0.5 StDev=0.7 Night Mean=10.3 StDev=16.8 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=1.8 StDev=4.4 Night Mean=14.6 StDev=33.4 

SUBJECT # 9 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=7.7 StDev=21.1 Median=1.5 Night Mean=78.7 StDev=256.8 Median=0.6 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

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240

260

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320

More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=21.6 StDev=60.5 Median=4.1 Night Mean=35.4 StDev=100.5 Median=0.5 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

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260

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=7.4 StDev=8.8 Median=4.6 Night Mean=75.2 StDev=187.5 Median=4.6 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=15.0 StDev=23.8 Median=7.0 Night Mean=195.4 StDev=345.8 Median=32.1 

0 

123456

Counts 

Figure 4.18: Steering wheel angle power 1 to 5 minutes before crash compared to baseline data from m

81
 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 10
 

0 

123456

Counts 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=14.6 StDev=54.6 Median=1.1 Night Mean=39.6 StDev=71.0 Median=13.9 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=17.3 StDev=51.2 Median=1.5 Night Mean=56.8 StDev=116.0 Median=9.9 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=11.2 StDev=34.8 Median=1.9 Night Mean=80.3 StDev=229.1 Median=5.8 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=2.9 StDev=5.9 Median=0.7 Night Mean=51.6 StDev=167.8 Median=4.4 

SUBJECT # 11
 

0 

123456

Counts 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=3.0 StDev=6.3 Median=0.3 Night Mean=10.6 StDev=16.8 Median=4.3 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=0.7 StDev=1.4 Median=0.2 Night Mean=34.4 StDev=67.9 Median=8.9 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.4 StDev=0.8 Median=0.2 Night Mean=60.4 StDev=146.2 Median=2.1 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=1.1 StDev=1.4 Median=0.4 Night Mean=6.7 StDev=11.2 Median=2.1 

SUBJECT # 11
 

0 

123456

Counts_ 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=1.8 Median=0.2 Night Mean=60.0 StDev=207.9 Median=1.0 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=3.7 StDev=7.6 Median=1.2 Night Mean=11.7 StDev=25.2 Median=2.9 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=0.5 StDev=1.0 Median=0.2 Night Mean=26.6 StDev=63.0 Median=5.9 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=3.8 StDev=10.2 Median=0.8 Night Mean=71.0 StDev=84.7 Median=37.0 

Figure 4.18 (continued) 

82 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 13
 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=7.3 StDev=13.6 Median=3.2 Night Mean=51.8 StDev=103.3 Median=8.4 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=4.0 StDev=5.4 Median=2.1 Night Mean=13.6 StDev=18.2 Median=8.8 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

300

320

More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=10.9 StDev=21.2 Median=3.2 Night Mean=15.4 StDev=38.3 Median=2.3 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

280

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=5.9 StDev=11.1 Median=2.5 Night Mean=12.0 StDev=20.1 Median=2.8 

0 

123456

Counts 

SUBJECT # 14
 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

240

260

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More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=3.1 StDev=4.1 Median=1.4 Night Mean=5.1 StDev=8.3 Median=1.1 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

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220

240

260

280

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=3.7 StDev=5.0 Median=1.5 Night Mean=1.3 StDev=1.7 Median=0.8 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

180

200

220

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260

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=4.9 StDev=7.5 Median=1.6 Night Mean=9.9 StDev=21.0 Median=0.5 

0 

123456

Counts 

SUBJECT # 16
 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=10.8 StDev=17.5 Median=3.3 Night Mean=44.8 StDev=151.8 Median=2.3 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

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More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=5.5 StDev=9.8 Median=1.3 Night Mean=15.6 StDev=26.9 Median=4.9 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

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180

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=5.9 StDev=14.5 Median=2.5 Night Mean=27.0 StDev=60.8 Median=6.4 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=3.4 StDev=11.3 Median=0.8 Night Mean=26.7 StDev=49.0 Median=6.7 

0 

123456

Counts 

Figure 4.18 (continued) 

83
 
Morn ng

Mean 10.6StDev 17 1NighMean 62.1S Dev 167.6

Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6Morn ngMean 10.6StDev 17 1NighMean 62.1S Dev 167.6

SUBJECT # 17
 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=10.5 StDev=18.6 Median=4.1 Night Mean=61.9 StDev=98.1 Median=21.6 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

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More 

Power Bins (Degree2) 

Counts Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=3.7 StDev=5.5 Median=1.9 Night Mean=74.4 StDev=87.7 Median=35.5 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

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180

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220

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260

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=5.9 StDev=8.4 Median=2.5 Night Mean=95.4 StDev=107.3 Median=58.2 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

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120

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More 

Power Bins (Degree2) 

Counts Night Morning 

Morning Mean=4.3 StDev=5.8 Median=1.7 Night Mean=43.3 StDev=54.0 Median=19.6 

0 

123456

Counts 

SUBJECT # 19
 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=0.9 StDev=1.3 Median=0.3 Night Mean=6.3 StDev=11.6 Median=1.6 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

40

60

80

100

120

140

160

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Power Bins (Degree2) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=9.3 StDev=28.7 Median=1.2 Night Mean=19.7 StDev=48.4 Median=3.8 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

40

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Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=2.0 StDev=2.7 Median=1.1 Night Mean=70.0 StDev=212.0 Median=1.2 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

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Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=3.3 StDev=6.3 Median=1.5 Night Mean=38.7 StDev=60.9 Median=14.3 

0 

123456

Counts_ 

SUBJECT # 20
 

Steering Wheel Angle Instantaneous Power Histogram 

5-4 minute before crash 

0 

10 20 30 40 50 60 

20 4060 80100120140160180200220240260280300320More 

Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=6.8 StDev=12.3 Median=1.5 Night Mean=11.3 StDev=20.7 Median=3.4 

Steering Wheel Angle Instantaneous Power Histogram 

4-3 minute before crash 

0 

10 20 30 40 50 60 

20

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Power Bins (Degree2) 

Counts_ Nigh Morning 

i 

= 

= . 

t 

= 

t = 

Morning Mean=5.1 StDev=9.8 Median=1.7 Night Mean=12.6 StDev=22.8 Median=2.8 

Steering Wheel Angle Instantaneous Power Histogram 

3-2 minute before crash 

0 

10 20 30 40 50 60 

20

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Power Bins (Degree2) 

Counts_ Night Morning 

Morning Mean=2.1 StDev=4.0 Median=0.6 Night Mean=20.1 StDev=48.8 Median=2.2 

Steering Wheel Angle Instantaneous Power Histogram 

2-1 minute before crash 

0 

10 20 30 40 50 60 

20

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Counts_ Night Morning 

Morning Mean=1.4 StDev=2.3 Median=0.6 Night Mean=5.8 StDev=12.9 Median=0.5 

0 

123456

Counts_ 

Figure 4.18 (continued) 

84
 
SUBJECT # 7 SUBJECT # 8 SUB

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

2148 2158 2168 2178 2188 2198 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

SUBJECT # 10 SUBJECT # 11 SUB

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 

3874 3884 3894 3904 3914 3924 

Time (Second) 

STEER (Degree) Night

 CRASH 

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 

8857 8867 8877 8887 8897 8907 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

Steerin

1-0 m

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

5325 5335 5345 

Tim

STEER (Degree)_ 

Steerin

1-0 m

-50 -40 -30 -20 -10 

0 

10 20 30 40 

7271 7281 7291 

Tim

STEER (Degree)_ 

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 30 

3571 3581 3591 3601 3611 3621 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

Figure 4.19: Dozing off periods (marked by circles) during one minute before a crash for each 

85
 
SUBJECT # 13 SUBJECT # 14 SUB

SUBJECT # 17 SUBJECT # 19 SUB

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

2593 2603 2613 2623 2633 2643 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

Steerin

1-0 m

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

6587 6597 6607 

Tim

STEER (Degree)_ 

Steerin

1-0 m

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

8205 8215 8225 

Tim

STEER (Degree)_ 

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

670 680 690 700 710 720 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 

6461 6471 6481 6491 6501 6511 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

Steering Wheel Angle Signal 

1-0 minute before crash 

-50 -40 -30 -20 -10 

0 

10 20 30 40 50 

6727 6737 6747 6757 6767 6777 

Time (Second) 

STEER (Degree)_ Night

 CRASH 

Figure 4.19 (continued) 

86
 
Video Recording of Driver Behavior 

Infrared cameras placed inside the vehicle cabin continuously monitored the motion of 

drivers’ hands, body, feet, and face during testing. A total of 26 hours of video stream 

was recorded during experiment. Comprehensive analysis of this large volume of video 

data was outside of the scope of this study. However, analysis of the video recordings for 

two of the tested drivers was performed to give the reader an insight of the available data 

and possible information that can be drawn from it. 

Figure 4.20 shows images of drivers in advanced drowsiness states recorded during 

experiment. 

Figure 4.20: Screenshots of drowsy drivers (top left: driver yawning, top right: 

driver sleeping, bottom left: driver scratching eyes, bottom right: driver with eyes 

closed) 

To analyze the video data, the research team classified drivers’ actions in several 

categories, as listed in Table 4.12. These categories include head and body postures, 

signs of boredom, mirror checks, driver interaction, and signs of fatigue. 

Since drivers drove longer in night sessions than in morning sessions, the number of 

occurrences of each driver’s behavioral variable was normalized by time. Values in 

Table 4.12 are per hour of driving. Bold numbers are used for night sessions data. 

Analysis of this data is intricate due to the varying driver behaviors and driving 

strategies. 

87
 
Table 4.12: Drivers’ behavioral variables identified through video data (number of occurrences 

Head and body postures Signs of boredom Mirror check

SUBJ. 

Head forward motion

Head backward motion 

Stretch neck

Stretch arms 

Stretch body

Stretch shoulders 

Touch / scratch mouth 

Touch / scratch nose 

Touch / scratch chin

Touch / scratch face 

Touch / scratch head/ears 

Touch / scratch eyes 

Touch / scratch legs 

Touch / scratch arms 

Touch / scratch neck 

Touch / scratch chest 

Touch / scratch body

Body motion

Readjust seating

Head inclined towards left 

Head inclined towards right 

Readjust clothing 

Sigh 

Check time

Look left mirror 

Look right mirror

Lk tl l

2 37.2 0 4.7 1.2 2.3 2.3 1.2 1.2 0 1.2 1.2 2.3 0 1.2 1.2 0 0 16.3 14 0 0 0 1.2 1.2 155.8 46.5 23

2 15.1 0.4 4.9 2 1.6 0.8 2.4 1.6 1.6 9 7.8 5.7 3.3 0.8 1.2 0.8 1.2 12.7 15.9 2 1.6 0.8 0.8 2.4 79.6 43.7 3

7 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 3 4 0 0 0 10 0 370 123 1 

7 1.1 0 0.6 1.7 0 0.6 0.6 1.1 0 1.1 1.7 2.2 0.6 0 0 0 0 3.3 3.3 12.8 0.6 0 0.6 0 371.6138.3 1

88
 
Head and Body Posture 

The head and body posture of a driver can be indicative of drowsiness. Driver No. 2 and 

Driver No. 7, for example, exhibited a range of this type of behavior. 

Driver No. 2 showed a high number of head motions during testing. For example, this 

driver exhibited a reflexive head nod after checking her side mirrors. Her head motions 

are significantly less frequent during night session than morning session, implying a 

possible correlation with drowsiness. The number of times Driver No. 2 touches or 

scratches her chin, face, head, ears, eyes, and legs significantly increased in the night 

session comparing to the morning session. These behaviors were especially frequent 

during drowsy periods and can be interpreted as actions used by Driver No. 2 to fight 

against fatigue. 

Similar observations could be made for Driver No. 7 with smaller differences between 

night and morning driving. However, Driver No. 7 adopted a different behavior during 

drowsy periods as he was very often inclined to turn his head to the left to relieve 

muscular tension from his neck. Seating positions did not change for both drivers 

between morning and night sessions. Drivers readjusted their seating on a regular basis 

after some time spent behind the wheel. This behavior does not appear to be related to 

drowsiness/fatigue but is only a function of time spent driving. 

Signs of Boredom 

The video of the drivers can also provide some capability to differentiate between fatigue 

and monotony. Driver No. 2 checked the time more frequently during night session as 

compared to morning session, especially at the end of the experiment. Driver No. 7 

sighed more during the morning session. Although boredom usually depends on the time 

spent at task, it is complex to analyze many factors external to the driving simulation that 

can affect drivers’ boredom. Driver No. 7 could, for example, have shown signs of 

impatience during the morning session because he knew he had to go to his regular work 

day after testing, while for the night session he was not preoccupied by his work schedule 

after the experiment. 

Mirror Checks 

Observations of drivers’ behaviors inside the truck cabin confirmed the importance of 

rear vision for truck driving simulation, as drivers spontaneously checked their side 

mirrors on a very regular basis when driving. Mirror checking activity decreased during 

night driving for Driver No. 2 possibly indicating that drowsiness can affect drivers’ 

mirror checking activity. No change could be observed between morning and night 

sessions for Driver No. 7. 

Driver Interaction and Signs of Fatigue 

For both drivers, the videos confirm that eye blinking activity radically increases during 

drowsy periods. More eye blinks and longer eye blinking times could be observed during 

the night sessions comparing to the morning sessions. Also, episodes of yawning were 

more frequent during night sessions, especially for a few minutes before drowsy periods. 

89
 
Yawning, if it could be easily detected, would probably be a good input for a drowsiness 

detection system. 

The drivers’ hands position on the steering wheel also revealed interesting patterns. 

Depending on their fatigue level, drivers seemed to place their hands differently on the 

steering wheel. It could, for example, be observed that alert drivers would mostly drive 

with their hands placed in the right “10:2” position, as shown at the top of Figure 4.20. 

As shown in the bottom of the figure, drowsy drivers, on the other hand, tend to adopt a 

more relaxed position. For example, when drowsy, these subjects drove with both hands 

at the bottom of the steering wheel, with the right hand steering and the left arm on the 

side window, or leaning on the steering wheel and driving with the forearms. 

Figure 4.20: Examples of drivers’ hands positions on the steering wheel depending 

on their fatigue level 

Although this video data was primarily collected in this study to help the research team 

identify drowsy steering data to be used in the Artificial Neural Network detailed in the 

next chapter of this report, the authors believe that these video recordings contain 

additional and valuable information that would require more work to be analyzed. 

Data Analysis Summary 

The four variables of measures, PERCLOS, vehicle crash, vehicle lateral displacement, 

and steering wheel angle, showed some correlation with the driver’s drowsiness. These 

variables or measures could be potentially used for drowsiness detection purposes. As 

mentioned earlier (see section on Eye Closure Data and PERCLOS in this chapter), 

PERCLOS is not an ideal candidate since capturing eye image is an intrusive process and 

the measuring methods may present limitations. On the other hand, unobtrusive 

measurements of lateral displacement and steering wheel angle position can be good 

90
 
candidates to evaluate drowsiness in a detection system. The number of crashes directly 

relates to the driver’s drowsiness; however, this data are not suitable for use in a collision 

prevention system, but is a useful indicator in this study for the onset of sleep episodes. 

Variations in vehicle lateral position are a direct consequence of the variations in steering 

angle. Therefore, only one of them should be used as a drowsiness measure. Steering 

angle was selected due to the ease of data acquisition. 

Sensing of vehicle lateral position is a very complicated process as compared to sensing 

of steering angle. Two techniques can be cited as examples. In the first method, two 

video cameras are mounted on front of the vehicle to capture the image of road marking 

lines. The image is then digitized and processed by an algorithm to determine the 

position of the vehicle with respect to the center of the lane. In the second method, a 

magnetic sensor detects the vehicle position with the help of magnets buried in the 

roadway. Both methods require expensive hardware while sensing of steering angle only 

needs a potentiometer in the steering column. Capturing of a road image can be very 

challenging under various road and weather conditions. In addition, putting magnets in 

the highways across the nation will be a very expensive and is therefore infeasible. 

Steering can be very accurately detected irrespective of the outside conditions. 

Therefore, a system based on vehicle lateral displacement will be very costly and 

complicated. On the other hand, steering-based systems will require negligible 

maintenance as compared to systems based on vehicle lateral displacement. 

91
 
4. DEVELOPMENT OF ALGORITHMS FOR DRIVER 

DROWSINESS DETECTION 

The technical approach for the development of the driver drowsiness detection algorithm 

is based on applying Artificial Neural Networks (ANN) to learn driver steering 

performance. Measuring driver performance is very challenging due to the complexity, 

variability, and highly non-linear nature of human behavior. Establishing an exact 

relationship between a driver’s steering activity and driver state of alertness is very 

difficult as it involves learning human behavior. Adaptive learning techniques, such as 

ANNs, which can learn from examples and predict the output without knowing the exact 

relationship between input and output, are ideally suited for modeling human behavior. 

These techniques present significant advantages over conventional statistical and 

mathematical methods for capturing the details of human behavior and establishing 

precise equations relating steering angle activity to drowsiness. 

ANNs have been studied and utilized in numerous scientific and engineering fields. One 

of the main advantages of ANNs is that they infer solutions from data without prior 

knowledge of the patterns in the data; they extract the patterns empirically, even if the 

equation between the inputs and the outputs does not exist. This characteristic is very 

important because in most practical cases, where the exact input-output relationship is 

very difficult to establish. ANNs also have the ability to generalize (i.e., they respond 

with a reasonable accuracy to patterns that are broadly similar to the original training 

patterns). Generalization is very useful because real world data is noisy, distorted, and 

often incomplete. ANNs are nonlinear, that is, they can solve some complex problems 

more accurately than linear techniques do. Nonlinear behavior is common but can be 

very difficult to handle mathematically. 

In this study, ANNs were trained to learn the steering behavior of drivers driving under 

different levels of sleep deprivation in order to identify drowsy driving behavior. The 

success of the method depends on a unique data-preprocessing scheme, which enables the 

use of a single ANN for all drivers, by incorporating the variability in human behavior. 

Two models were developed – one with steering only data and the second one with 

steering and eye closure data. 

Both the steering-with-eye-tracking ANN and the steering-only ANN performed well, at 

levels analogous to the previous passenger vehicle detection systems. The steering-witheye-tracking ANN had an accuracy of 88% with a false alarm rate of 9%. The steeringonly ANN had an accuracy of 85% with a false alarm rate of 14%. 

The remainder of this chapter provides a detailed description and evaluation of the ANNs 

used for the drowsy driver detection system. Specifically, this chapter provides 

background on artificial neural networks; the data preprocessing scheme used for these 

ANNs; the neural network architectures; details of their training, validation, and testing; 

and an evaluation of the drowsy driver detection system in the truck simulator 

experiment. 

92
 
-1 

x1 

x2 

x8 

Input Layer Hidden Layer Output Layer 

-1 

wji 

i 0 

ARTIFICIAL NEURAL NETWORK MODELS 

The architecture of the neural network used in this model is shown in Figure 5.1. The 

network consists of three layers of neurons. 

Input layer: The main function of this layer is input fanning. There is no weight vector 

associated with the input layer. The number of elements is set equal to the dimension of 

the input vector X(n), as explained in the next section. This layer accepts the input and 

passes it on to the next layer without any processing. 

Hidden layer: This layer is connected to the input layer and output layer by a full 

synapse. The number of neurons in this layer is selected based on a sensitivity analysis. 

Input to this layer is the output of the input layer multiplied by the n-dimensional weight 

vectors of the hidden layer neurons. The output of this layer is an n-dimensional vector. 

Figure 5.1: Artificial neural network architecture 

Output layer: The number of neurons in this layer corresponds to the number of 

dimensions of the desired output vector. Input to the neurons of this layer is the ndimensional vector (output of hidden layer) multiplied by the n-dimensional weight 

vector of the output layer neurons. During training, the output layer is provided with the 

desired output vector. The output of this layer is an n-dimensional vector. 

93
 
 

These layers are connected by full synapse (i.e., each input layer neuron is connected to 

each neuron of the hidden layer and similarly the hidden layer is fully connected to the 

output layer). The first neuron with –1 as input in Figure 5.1 represents the threshold or 

bias. 

Training Algorithm (Supervised ANN) 

For training, the network weights and thresholds are updated using an error back 

propagation-learning algorithm. This algorithm is summarized as follows: 

Wji = Synaptic weight connecting neurons i and j. 

vj = Internal activity of neuron j. 

yj = Output of neuron j. 

n = Iteration Number 

1.	 Initialize the synaptic weights and the thresholds to small random numbers. 

2.	 Present the network with an epoch of training exemplars. 

3.	 Apply input vector X(n) to the input layer and the desired response D(n) to the output 

layer of neurons. The output of each neuron is calculated as follows: 

y j(n) = φ(v j(n))	 (5.1) 

φ() is the activation function, which maps the neuron’s input infinite domain to a prespecified range. 

p
 

v j (n) = ∑ w ji (n)xi (n) i=0
 

where 

(5.2) 

p = No.of neurons in the layer
 

x i (n) is input to neuron i
 

For the output layer, the error signal is computed as: 

ek (n) = dk (n) − ok (n)	 (5.3) 

where ok(n) is the output of neuron i in the output layer, and dk(n) is the desired 

output for neuron i in the output layer. 

94
 
4.	 Adjust the synaptic weights and thresholds of the network by back propagating the 

error through the network. Weights are adjusted by the amount Δw given by equation 

(5.4). 

1 N 

Δw = ∑ δ j(n)xi (n) (5.4) ji N n=1 

where δj(n) is the local gradient given by: 

δk (n) = ek (n)φ

/ (vk (n)) for the output layer	 (5.5) 

m 

δ j(n) = φ

/ (v j

(n)) ∑ δk (n)wkj(n) for the hidden layer (5.6) k=1 

m = No. of neurons in the output layer. 

N = No. of training sets in one epoch. 

The new weights are given by: 

w ji (n +1) = w ji (n) + ηΔw ji (n) + αΔw ji (n −1)	 (5.7) 

where η is the learning rate parameter. The value of this parameter affects the 

convergence rate and is determined experimentally, and α is the momentum constant. 

Typically the value is between 0.1 and 0.9. This term enables the training process to 

skip local minima and move toward the global minima. 

5.	 Iterate the computation by presenting new epochs of training examples until the mean 

square error (MSE) computed over entire epoch achieves a minimum value. MSE is 

given by: 

1 N m 2 MSE = ∑ ∑ e k (n) (5.8) 2N n=1 k=1 

Training Parameters 

There are many parameters that affect the training performance of an ANN. Some of 

these parameters are discussed. For more details, please refer to Haykin, S. (1994). 

Transfer Function 

The transfer function, also called activation function, maps the neuron’s input (possibly) 

infinite domain to a pre-specified range. Although the number of activation functions can 

95
 
 

 

 

 

 

be infinite, four famous functions are regularly employed for most engineering 

applications: 

i- Linear Function: The linear function produces a linearly modulated output from the 

input x as described by the relation: 

f ( x ) = ax (5.9) 

where x ranges over the real numbers, and a is a real scalar. 

ii- Step Function: The step function, produces only two values, b and - c . If the input 

x equals or exceeds a predefined value θ , then the function produces the value b ; 

otherwise it produces the value - c , where b and c are positive scal ars. Mathematically 

this function is described as: 

⎧ b if x ≥ θ

f ( x) = ⎨ (5.10)

⎩− c if x < θ

For the assignments b = 1 , c = 0 , and θ = 0 , the step function becomes the binary step 

function. It can be bi-directional binary step function if c =1. 

iii- Ramp Function : The ramp function is a combination of the linear and step functions. 

It places upper and lower bounds on the values that the function produces and allows a 

linear response between the bounds. These saturation points are symmetric around the 

origin and are discontinuous at the points of saturation. The ramp function is defined as: 

⎧ γ if x ≥ γ
 ⎪ < γ (5.11) 

⎪

f ( x ) = ⎨ x if x 

− γ if x ≤ −γ ⎩

Where γ is the saturation value for the function. 

iv- Sigmoid Function: The sigmoid function is a S-shaped continuous version of the 

ramp function and is a bounded, monotonic, non decreasing function that provides a 

graded, nonlinear response within a prespecified range. The most c ommon sigmoi d 

function is the logistic function and described mathematically as: 

1 f ( x ) = − ax (5.12) 1 + e 

Where a is a positive scalar called steepness factor (usually a = 1 ). The logistic function 

provides an output value from 0 to 1. When a becomes ∞ in equation (5.12), the slope 

of the sigmoid function between 0 and 1 b ecomes infinitely steep and, in effect, it 

becomes the unipolar step function. An alternative to the logistic s igmoid functio n is the 

hyperbolic tangent function: 

f (x) = tanh(ax) (5.13) 

96
 
This is a special case of the logistic function. The hyperbolic tangent function shown in 

Figure 5.2 is symmetric about the origin and preferred for this reason in many 

engineering applications. 

xf )( 

x 

Figure 5.2: Hyperbolic Tangent Function 

Effect of Learning Rate 

The effectiveness and convergence of the error back-propagation learning algorithm 

depend significantly on the value of the learning rate η . While gradient descent can be 

an efficient method for obtaining the weight values that minimize the output error, error 

surfaces frequently possess properties that make the procedure slow to converge. 

When broad minima yield small gradient values, then a larger value of η will result in a 

more rapid convergence. However, for problems with steep and narrow minima, a small 

value of η must be chosen to avoid overshooting the solution. This leads to the 

conclusion that η should be chosen experimentally for each problem. Some successful 

adaptive techniques have been reported in the literature to adaptively change the learning 

rate during the training process in order to accelerate the convergence and therefore 

reduce the training time for the BP networks (Haykin, 1994). 

Effect of the Momentum Term 

The momentum term allows the network to respond not only to the local gradient, but 

also to the recent trends in the error surface. This term enables the training process to 

skip the local minima and move toward the global minimum of the error surface. 

Typically, α is chosen between 0.1 and 0.9. The momentum term technique is 

recommended for problems that converge too slowly or for cases when learning is 

difficult to achieve. 

Cross Validation 

During training, there is always a tendency for the network to memorize the training data 

and thus fail to generalize beyond this training data. The result of this memorization is 

that the network performs well only on the data on which it is trained. If it is presented 

with a data that does not exactly match the training set, the network classification 

accuracy will be greatly reduced. In order to keep the network from memorizing the 

97
 
training data, a separate set of input-output examples is used for cross validation. Every 

time the weights are updated, MSE calculated from the cross validation data set (which is 

not a part of the training set) has to be compared with the MSE calculated from the 

training data set. At any point during training, if the cross validation MSE exceeds the 

training MSE, training must be stopped because this is an indication of network 

memorization. A set of data points should be set aside for cross validation during 

training. 

ANN MODEL BASED ON STEERING ANGLE DATA 

This Artificial Neural Network model was trained on data from steering angle only. Data 

from the steering angle was preprocessed and converted into a vector state representing n 

seconds of steering activity before being presented to the model. Training, data 

preprocessing, and testing phases are discussed in this section. Figure 5.3 shows a 

schematic of the ANN model based on steering angle data. 

Figure 5.3: ANN model based on steering angle 

DATA PREPROCESSING AND INPUT DISCRETIZATION 

(STEERING ANGLE) 

The successful training of an artificial neural network depends on selecting and preparing 

proper set of the input data for training. Both steering angle and eye closure data were 

preprocessed before presenting to the ANN, which allowed a vector state presentation. 

The data was preprocessed in two steps before presenting to the ANN. In the first step, 

effect of road curvature was normalized. In the second step, the data was discretized and 

coded to allow a vector state presentation of the steering wheel angle. 

Effect of Road Curvature and Lane Changes 

Road horizontal geometry normally includes two types of geometric sections, straight 

lines and curves. To simulate the real driving conditions, the test scenario used in the 

experiment included both straight and curve sections. The steering angle signal recorded 

in the experiment, therefore, contains two different types of waveforms. In the straight 

Steering Angle Normalize for 

Road 

Curvature 

Discretization 

and 

Coding 

Preprocessing 

A 

N 

N 

M 

O 

D 

E 

L 

WAKE 

DROWSY 

Vector

 15 sec of Steering 

Angle Data 

98
 
-15 

-10 

-5 

0 

5 

10 

15 

20 

1 51 101 151 201 251 

Steering Angle (Deg) 

Actual Data After Subtracting Curvature 

Right Curve 

Left Curve 

sections, the waveforms consist only of the constant steering adjustments to keep the 

vehicle in the center of the lane, while in the curve sections, the waveforms contain road 

curvature, in addition to the component of steering adjustments for lane keeping. Data 

from the curve sections has to be normalized for the effect of curvature. Figure 5.4 

shows the two waveforms and the signal after removing the curvature effect. 

Time (Sec) 

Figure 5.4: Steering angle signal 

The effect of curvature was normalized by subtracting the road curvature from the 

steering angle in the curve sections. This procedure also removed the effect of lane 

changes from the steering data. The procedure involved is explained as follows: 

If four or more consecutive data points are of the same sign (positive for right turn and 

negative for left turn) and their sum is greater than or equal to 15 (absolute) degrees, then 

all these points were assumed to be from a curve or portion of a curve section. The mean 

value of these data points was than subtracted from each of these points. Mathematically, 

it can be formulated according to the following model: 

99
 
 

 

Let s1, s2, s3, s4, ...........sn-1, sn be consecutive points of steering wheel angle data 

IF sgn(s1) = sgn(s2) = sgn(s3) = sgn(s4) =.................... sgn(sn-1) ≠ sgn(sn) 

AND (s1) + (s2) + (s3) + (s4) +.................... (sn-1) ≥ 15 

THEN, FOR 

i = 1 to n – 1 

n−1 

∑sj 

j=1

si = si − (5.14) 

n −1
 

Table 5.1 shows an example of this procedure. 

Table 5.1: Example of steering angle correction in curve section 

Steering angle Mean Corrected angle 

23.5 0

 23 -0.5

 24 0.5

 23 -0.5

 22.5 -1

 23 -0.5

 23.5 0

 23 -0.5

 23.5 23.5 0

 24 0.5

 23 -0.5

 25 1.5

 22.5 -1

 24 0.5 

More and more of the modern vehicles are equipped with navigation systems with GPS 

and GIS (Geographic Information System) assistance. The road curvature information 

can be obtained in advance and proper compensation made when the curve section is 

actually reached. Also, lane changes can be identified in real-time by the drivers’ 

activation of the turning signals. Steering data could then be compensated for the effect 

of lane change maneuvers. 

100
 
Input Discretization (Steering Angle) 

In step two, the one-dimensional steering angle data was discretized and coded into an 

eight-dimensional vector. 

I(T) = [ i1, i2, .......,i8 ] 

Discretization is performed according to the following scheme: 

Let r1, r2, ......., r8 each represent a d iscrete range of steering wheel angle. These ranges are 

defined as follows: 

4 4 − ∑ pk > r

i ≥ − ∑ pk for i = 1 → 4 (5.15)

k=i k=i−1

4 4 

∑ pk ≤ r

i < ∑ pk for i = 5 → 8 (5.16)

k=9−i k=8−i 

where pk , (k = 0....4) are constants. 

These constants were used to define the discretization ranges. Some drivers are more 

sensitive to vehicle lateral position and make very accurate corrections to the steering for 

lane keeping while others are less sensitive and make less accurate corrections. The 

result is a low amplitude signal for more sensitive drivers and relatively high amplitude 

signal for less sensitive drivers. Larger values for pk will make the discretization ranges 

wider to accommodate large amplitude while small values will make them shorter for 

small amplitudes. 

With: 

p0 > 90 degree (upper limit on steering angle) 

p1, p2, and p3 are selected according to the driving behavior of individual driver 

p4 = 0 (lower limit of steering angle) 

101
 
 

Table 5.2 shows these ranges specifically for p1 = p2 = p3 =1. Table 5.3 shows the 

values of the constants used for discretization. 

Table 5.2: Ranges for p1=p2=p3=1 

ri Range 

r1 -3 > r1 ≥ -93 

r2 -2 > r2 ≥ -3 

r3 -1 > r3 ≥ -2 

r4 0 > r4 ≥ -1 

r5 0 ≤ r5 < 1 

r6 1 ≤ r6 < 2 

r7 2 ≤ r7 < 3 

r8 3 ≤ r8 < 93 

Table 5.3: Values of constants used for discretization 

Subject # P1 P2 P3 

2 4 4 4 

7 7 7 7 

8 4 4 4 

9 4.5 4.5 4.5 

10 6 6 6 

11 4 4 4 

13 5 5 5 

14 4 4 4 

15 4 4 4 

16 4 4 4 

17 4.5 4.5 4.5 

19 5 5 5 

20 4.5 4.5 4.5 

21 

102
 
Input Coding (Steering Angle) 

After defining the discrete ranges, steering angle was coded into an eight-dimensional 

vector I(T). The coding was performed according to the following scheme: 

If the amplitude of steering angle falls within the range represented by ri then the ith 

component of I(T) was set equal to 1 and the remaining 7 components were set equal to 

zero. For example, if the steering angle is 2.5 degrees and p1 = p2 = p3 =1, then it will 

fall in the range represented by r7 (2 ≤ r7 < 3), therefore, only the 7th component of I(T) 

will be 1 and all other components will be 0, i.e. 

I
 

0
 

0
 

0
 

0
 

0
 

0
 

1
 

0
 

Table 5.4 shows input discretization and coding for a set of values of steering angle. 

Table 5.4: Discretization and coding of steering angle data for p1=p2=p3=1 

Time Steering I(T) = [ i1, i2, .............,i8 ] 

T sec Angle i1 i2 i3 i4 i5 i6 i7 i8 

1 1.1 0 0 0 0 0 1 0 0 

2 -3.1 1 0 0 0 0 0 0 0 

3 -2.2 0 1 0 0 0 0 0 0 

4 0.8 0 0 0 0 1 0 0 0 

5 -1.7 0 0 1 0 0 0 0 0 

6 3.0 0 0 0 0 0 0 1 0 

7 1.1 0 0 0 0 0 1 0 0 

8 -3.1 1 0 0 0 0 0 0 0 

9 -2.2 0 1 0 0 0 0 0 0 

10 0.8 0 0 0 0 1 0 0 0 

11 -1.7 0 0 1 0 0 0 0 0 

12 3.0 0 0 0 0 0 0 1 0 

13 0.8 0 0 0 0 1 0 0 0 

14 -1.7 0 0 1 0 0 0 0 0 

15 3.0 0 0 0 0 0 0 1 0 

X(n) 2 2 3 0 3 2 3 0 

⎡
 ⎤

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥
 

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢
 

=
 

⎣ ⎦

103
 
 

Input Data (Steering Angle) 

After coding, vector I(T) was summed over n data points to get the input vectors X(n). 

X(n) = I(T) + I(T +1) + I(T + 2 ) +LLLLI(T + n −1) (4.17) 

Data was collected at 20 Hz and was averaged over a one second interval. This signal 

downsampling did not affect the performance of the neural network and allowed the 

model to achieve similar results with smaller amounts of data. For n =15, X(n) now 

represents 15 seconds of steering activity. 

Capturing individual human behavior is a complex phenomenon. Selection of proper 

values for these constants (p1, p2, and p3) is a topic for further research. Further 

experiments, with a more diverse population of drivers, are required to study the 

individual behavior. With more knowledge of individual behavior, we may be able to 

classify the drivers into groups with similar behavior. With limited data and a 

population, which is not very diverse, it was not possible to classify drivers based on 

individual performance. The values of these constants were selected based on visual 

observation of data and by trial and error. 

By changing the values of these constants, the sensitivity of the system can be adjusted. 

Smaller values will make the system more sensitive and may result in increase number of 

false alarms while large values will make it less sensitive. In a real life situation, one will 

start with a high sensitive system and gradually adjust the values until one is comfortable 

with the number of false alarms. 

It can be seen from Table 5.3 that the values of p1=p2= p3 ranged from 4 to 5 for the 

majority of the drivers who participated in this study. This input discretization scheme 

combines n seconds of steering angle changes (i.e., driver performance) and allows a 

vector state representation of this variable. This vector state also incorporates a measure 

for sensitivity of the individual driver steering, which enables the system to use the same 

network (ANN) for different drivers. By adjusting the sensitivity level for different types 

of drivers, the need for training a separate ANN for each driver was eliminated. 

Desired Output 

An artificial neural network requires a set of training examples consisting of an input and 

a desired output. The driving intervals represented now by X(n) are classified into two 

classes, drowsy and awake. Since no subjective evaluation or any other criteria for 

classification was available, the driver was assumed to be drowsy if: 

•	 He presented signs of advanced fatigue from test observations (see section on 

analysis of in-vehicle video recordings in previous chapter) 

•	 His eye closure data reflected a blinking activity associated with drowsiness 

These are good indicators of the driver performance. These were considered as qualifiers 

to indicate sleepy (drowsy) state. For the ANN training purpose, the outcome was 

classified as drowsy when these conditions were met. 

104
 
Most of the data from the end of the night sessions was classified as drowsy. Data from 

morning session was classified as awake, based on the observed data and lack of sleep 

deprivation for the drivers. The desired output is a two-dimensional vector, D(n). Each 

dimension represents each class. 

Table 5.5 shows desired output vector. Table 5.6 shows the format of the final input data 

to the network. 

Table 5.5: Desired output vector 

Awake Drowsy 

D(n) [1 0] [0 1] 

Table 5.6: Example of input data (steering only) 

X(n) D(n) 

x1 x2 x3 x4 x5 x6 x7 x8 Sleep Awake 

2 2 3 0 3 2 3 0 0 1 

0 1 0 14 0 0 0 0 1 0 

2 0 5 4 3 1 0 0 1 0 

0 0 2 3 9 1 0 0 0 1 

0 0 0 10 5 0 0 0 0 1 

0 5 3 6 1 0 0 0 1 0 

1 4 1 3 4 0 1 1 1 0 

1 5 2 0 5 1 1 0 1 0 

ANN ARCHITECTURE (STEERING ANGLE) 

The network architecture for this model was similar to the three layer, feed forward 

network shown in Figure 5.1. Table 5.7 shows a summary of the network configuration. 

Table 5.7: Summary of ANN architecture 

No. of Neurons 

No of Weights

Input Layer Hidden layer Output layer 

8 21 2 210 

105
 
TRAINING DATA SET (STEERING ANGLE) 

For training to be successful, equal proportions of examples from both classes (awake 

and drowsy) should be used. Data from 3 minutes after the start of the experiment and 3 

minutes before end of the experiment was discarded. The minimum data available from 

the morning session for every subject was approximately 30 minutes. Since all data from 

the morning session was classified as awake, only 30 minutes of data from selected 

portions of night driving was used for training. This corresponds to 100 exemplars from 

morning and 100 from the night. The number of input data required for training is 

roughly three times the number of weights in the network. The same number is required 

for testing network performance during training and about one third of this amount is 

required for cross validation. Data from 7 subjects (1,400 exemplars) was combined into 

one file, randomized, and then divided into three files. Table 5.8 shows the number of 

exemplars in each file. 

Table 5.8: Number of exemplars in each data set 

Number of Weights Training File Cross Validation Testing File 

210 600 200 600 

It is important to note that the purpose of the training file at this point is to test its 

performance during the iterative training process. The network is tested further using 

data from all subjects. 

ANN TRAINING (STEERING ANGLE) 

Training an ANN requires selecting the right architecture and optimum (close to 

optimum, best values) for the various training parameters. The ANN training was 

performed multiple times with varying parameters until the best results were obtained. 

For more detail analysis and understanding of ANNs, please refer to Haykin, S. (1994). 

Table 5.9 summarizes the training performance of the best or optimal ANN architecture. 

Table 5.9: ANN performance during training 

Best Networks 

Steering Only Model 

Training Cross Validation 

Epoch # 100 100 

Minimum MSE 0.0638 0.0559 

Final MSE 0.0638 0.0559 

106
 
The Steering Angle ANN had the following training characteristics: 

•	 The network was trained in multiple passes. Each pass lasted for a total of 1,000 

epochs or when the cross validation error exceeded the training error. 

•	 The network weights and thresholds were updated in batch mode (i.e., weights 

were updated after presentation of one complete epoch of data). 

•	 Input vector was normalized within (0, 1) range before presenting to the network. 

•	 The tan hyperbolic activation function, with output range from –1 to 1, was 

applied to neurons of the hidden and the output layers. The hyperbolic tangent 

function shown in Figure 5.2 is symmetric about the origin and preferred for this 

reason in many engineering applications. 

•	 Values for the learning rate parameter (η ) were kept variable through out the 

training process. Starting with a large value and gradually decreasing to a small 

value. 

•	 Momentum factor (α ) was also kept variable during training. 

Figure 5.5 shows Mean Square Error (MSE) for training and cross validation of the 

network. These graphs indicate the network performance during training. The cross 

validation error always stayed below the training error showing the network 

generalization. The smooth drop of MSE indicates that the ANN performed very well. 

MSE versus Epoch 

0 

0.05 

0.1 

0.15 

0.2 

0.25 

18

15222936435057647178859299 

Epoch 

MSE

Cross Validation MSE 

Training MSE 

Figure 5.5: ANN training performance 

107
 
 

ANN TESTING (STEERING ANGLE) 

During the training process, the ANN was tested after each iteration to evaluate its 

performance. Table 5.10 shows network testing performance for the best network 

weights. The test data set (test file) contained 600 input vectors (each vector representing 

15 seconds of driving). The test data was not used in training or cross validation; in other 

words, the network had never seen this data before. 

Table 5.10: Test results 

Output/Desired AWAKE DROWSY 

WAKE 288 40 

DROWSY 37 235 

% CORRECT 89 85 

The network correctly identified 235 out of a total of 275 drowsy intervals (i.e., an 

accuracy of 85%). There were 37 “false alarms”, intervals that were in the awake class 

but were misclassified as drowsy. Also, 40 intervals that were in the drowsy class were 

misclassified by the network as awake. 

ANN MODEL BASED ON STEERING AND EYE DATA 

This model is based on combined data from steering angle and eye closures. The purpose 

is to see the correlation between the two data sets and to see if the steering model can be 

further improved by adding eye closure data to the model. Steering data was 

preprocessed the same way as that of the steering angle model while eye data was 

processed as explained in the following section. Figure 5.6 shows the schematic of this 

model. 

Steering Angle Normalize for 

Road 

Curvature 

Discretization 

and 

Coding 

Preprocessing 

A 

N 

N 

M 

O 

D 

E 

L 

WAKE 

DROWSY 

Vector 

 15 sec of Steering 

Angle Data 

Discretization 

and 

Coding 

Eye Closure 

Vector 

 15 sec of 

Eye closure Data 

Figure 5.6: ANN model based on steering angle and eye closure 

108
 
 

 

 

EYE CLOSURE DATA 

As explained earlier, the eye tracking system records the pupil diameter, and a zero value 

means that the eyelid is closed. The data was recorded at 60 Hz during the experiment. 

In order to match with the steering angle data, it was reduced to 1 Hz, by using a new 

variable called Ci , where 

Ci = Number of zeros in one second of data (60 data points). 

This data, which now represent one second of eye closures, was used in further analysis. 

An example of a small portion of the eye closures data file is shown in Table 5.12. 

Input Discretization (Eye Data) 

Ci, which now represents one second of eye closures, was discretized into a fourdimension vector according to the following scheme: 

Let r9, r10, r11, and r12 each represent a discrete range. These ranges are defined as 

follows: 

9 9 

∑ pk ≤ r

i < ∑ pk for i = 9 →12 (5.18)

k=18-i k=17−i 

where pk , (k = 5....9) are constants. 

These constants were used to define the discretization ranges. The blink rate (frequency 

of blinking) and blink duration (amount of time the eye is continuously closed) are highly 

dependent on individual behavior. The constants pk are used to accommodate the 

variations in blink rate and blink duration. Larger values of pk will make the 

discretization ranges wider to accommodate large duration and high frequency, while 

smaller values will make them shorter for low frequency and shorter duration blinks. 

With: 

p5> 60 (upper limit is 60 Hz) 

p6 , p7 , and p8 are selected according to the driving behavior of individual driver 

p9 = 0 (no closures) 

Table 5.11 shows these ranges, specifically for p6 = p7 = p8 =6 

Table 5.11: Ranges for p 6 7 8 =p =p 
 =6 

ri Range 

r9 0 ≤ r 9 < 6 

r10 6 ≤ r10 < 12 

r11 12 ≤ r11 < 18 

109
 
r 12 1 8 ≤ r12 < 78 

110
 
Table 5.12: Example of Eye closure data 

T 

sec Pupil Diameter (60Hz) 

1 92 92 100 94 89 89 90 89 89 89 90 90 89 89 89 89 91 90 89 88 88 89 88 89 89 89 88 88 88 87 87 87 87 78 86 86 86 86 85 86 86 89 96 89 89 89 88 89 89 89 

2 0 0 0 0 0 78 89 90 91 92 91 92 92 92 92 93 92 95 93 94 94 93 93 93 96 93 93 95 93 94 93 93 94 93 94 94 99 0 90 95 92 93 93 92 91 93 92 93 92 0 

3 90 88 89 89 89 89 88 89 89 88 88 89 89 88 88 88 88 88 88 88 90 88 88 88 88 89 89 89 89 89 88 89 93 96 88 88 86 86 86 86 87 86 87 87 86 86 86 87 86 86 

4 81 81 81 81 81 80 80 0 80 80 79 0 0 0 0 0 0 0 0 79 79 79 80 0 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 81 80 81 80 0 0 0 0 0 0 

5 81 81 81 81 81 81 81 81 81 81 94 95 89 88 89 88 87 87 87 87 88 87 87 87 88 89 93 88 87 87 86 87 86 86 86 86 86 85 87 85 85 85 85 85 85 85 85 85 85 86 

6 86 86 85 85 85 87 85 88 90 85 84 83 83 83 83 0 83 83 83 83 82 83 82 82 82 81 80 80 80 79 80 79 79 79 83 76 77 76 76 76 76 76 76 76 76 76 75 76 76 76 

7 77 78 77 78 78 78 78 78 78 77 77 77 77 77 77 77 77 81 88 83 83 82 82 0 0 0 0 0 0 81 82 82 82 38 82 82 82 82 81 82 82 82 82 82 82 82 81 82 88 88 

8 75 75 75 74 75 75 74 74 74 75 74 77 73 73 73 73 73 73 73 73 0 73 73 73 73 75 74 74 74 74 74 74 74 74 75 75 75 75 75 76 75 76 76 76 76 76 77 76 76 76 

9 76 76 76 77 76 0 76 76 77 77 77 77 77 77 77 0 78 0 85 84 83 82 82 82 83 82 82 82 82 85 82 83 83 83 83 83 82 82 84 83 83 85 83 80 80 79 80 79 80 79 

10 72 71 71 71 71 71 71 71 70 71 71 70 71 71 73 72 72 72 72 72 72 72 72 72 72 72 81 80 78 77 78 76 78 77 77 78 77 78 78 78 78 78 79 80 79 80 79 80 79 80 

11 72 71 71 71 71 71 71 71 70 71 71 70 71 71 73 72 72 72 72 72 72 72 72 72 72 72 81 80 78 77 78 76 78 77 77 78 77 78 78 78 78 78 79 80 79 80 79 80 79 80 

12 72 71 71 71 71 71 71 71 70 71 71 70 71 71 73 72 72 72 72 72 72 72 72 72 72 72 81 80 78 77 78 76 78 77 77 78 77 78 78 78 78 78 79 80 79 80 79 80 79 80 

13 72 71 71 71 71 71 71 71 70 71 71 70 71 71 73 72 72 72 72 72 72 72 72 72 72 72 81 80 78 77 78 76 78 77 77 78 77 78 78 78 78 78 79 80 79 80 79 80 79 0 

14 72 71 71 71 71 71 71 71 70 71 71 70 71 71 73 72 72 72 72 72 72 72 72 72 72 72 81 80 78 77 78 76 78 77 77 78 77 78 78 78 78 78 79 80 79 80 79 80 79 80 

15 72 71 71 71 71 71 71 71 70 71 71 70 71 71 73 72 72 72 72 72 72 72 72 72 72 72 81 0 0 0 0 0 0 0 77 78 77 78 78 78 78 78 79 80 79 80 79 80 79 80 

111
 
 

 

 

 

 

 

 

 

 

 

 

 

Input Coding (Eye Data) 

After defining the discrete ranges, eye data (which is now represente d by Ci,) was coded 

into a four-dimensional vector E (T) as follows. 

If the value of Ci falls within the r ange repr esented by ri, then the ith component of E(T) 

was set equal to 1, and the remaining three co mp onents wer e set equal to zero. For 

example, if Ci = 7 and p6 =p7 = p 8 = 6 , then it wi ll fall in the ran ge represented by r10 (6 

≤ r9 < 12). Therefore, only the 2nd component of E(T) is 1 while all other components 

are zero, such as, 

0
 

1
 

0
 

0
 

⎤
 

⎥

⎥

⎥

⎥

⎦

⎡
 

⎢

⎢

⎢

⎢

E =
 

⎣

Table 5.13 shows input discretization and coding for a set of Ci values. 

Table 5.13: Discretization and coding of eye closure data for p2=p3=p4=6 

Time 

T sec 

Ci E(T) = [e1, e2, e3, e4] 

e1 e2 e3 e4 

1 4 1 0 0 0 

2 7 0 1 0 0 

3 0 1 0 0 0 

4 18 0 0 1 0 

5 0 1 0 0 0 

6 1 1 0 0 0 

7 6 0 1 0 0 

8 1 1 0 0 0 

9 3 1 0 0 0 

10 0 1 0 0 0 

11 0 1 0 0 0 

12 0 1 0 0 0 

13 4 1 0 0 0 

14 0 1 0 0 0 

15 7 0 1 0 0 

Input Data (Steering + Eye) 

The eight-dimensional steering angle vector I(T) and the four e onal eye closure -dim nsi 

vector E(T) were combined to form a twelve di me nsion vector J(T) as sho wn in Table 

5.14. After coding, vector J(T) was summed over an interval of n seconds to get the input 

vector X(n). Each input vector X(n) then represents n seconds of driving interval. 

X(n) = J T( ) + J T( +1) +
 J T( + 2) +
 LLLL
 J T( + n −1) (5.19) 

112
 
 

 

Table 5.14: Twelve dimension vector J(T) 

X(n) D(n) 

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 SLEEP WAKE 

2 2 3 0 3 2 3 0 11 3 1 0 0 1 

0 1 0 14 0 0 0 0 9 2 3 1 1 0 

2 0 5 4 3 1 0 0 0 1 5 9 1 0 

0 0 2 3 9 1 0 0 11 4 0 0 0 1 

0 0 0 10 5 0 0 0 11 3 1 0 0 1 

0 5 3 6 1 0 0 0 8 3 2 2 1 0 

1 4 1 3 4 0 1 1 7 3 2 3 1 0 

1 5 2 0 5 1 1 0 10 1 1 3 1 0 

Data was averaged for one second intervals. For n=15, X(n) now represents 15 seconds 

of stee ing and eye closure activity. r Table 5.15 shows a portion of the input file . 

Table 5.15: Example of input file for ANN (eye+steering) 

Time 

T sec Ci St. angle 

J(T) 

I(T) = [ i1, i2, .............i8 ] E(T) 

i1 I2 i3 i4 i5 i6 i7 i8 e1 e2 e3 e4 

1 4 1.1 0 0 0 0 0 1 0 0 1 0 0 0 

2 7 -3.1 1 0 0 0 0 0 0 0 0 1 0 0 

3 0 -2.2 0 1 0 0 0 0 0 0 1 0 0 0 

4 18 0.8 0 0 0 0 1 0 0 0 0 0 1 0 

5 0 -1.7 0 0 1 0 0 0 0 0 1 0 0 0 

6 1 3 0 0 0 0 0 0 1 0 1 0 0 0 

7 6 1.1 0 0 0 0 0 1 0 0 0 1 0 0 

8 1 -3.1 1 0 0 0 0 0 0 0 1 0 0 0 

9 3 -2.2 0 1 0 0 0 0 0 0 1 0 0 0 

10 0 0.8 0 0 0 0 1 0 0 0 1 0 0 0 

11 0 -1.7 0 0 1 0 0 0 0 0 1 0 0 0 

12 0 3 0 0 0 0 0 0 1 0 1 0 0 0 

13 4 0.8 0 0 0 0 1 0 0 0 1 0 0 0 

14 0 -1.7 0 0 1 0 0 0 0 0 1 0 0 0 

15 7 3 0 0 0 0 0 0 1 0 0 1 0 0 

X(n) 2 2 3 0 3 2 3 0 11 3 1 0 

113
 
ANN ARCHITECTURE (EYE+STEERING) 

The combined AN N model was ba sed on both steerin g angle and eye clos ure data. The 

network architecture is s imilar to tha t used in the steering only model with the following 

modifications: 

• The input layer has 12 neurons corresponding to the input vector X(n) 

• The hidden layer has 23 neurons. 

TRAINING DATA (EYE+STEERING) 

Data from eight subjects, which make a total of 1,600 driving intervals (as explained 

earlier), was randomized and then divided into three separate files (Table 5.16): 

• Training file, which includes a set of 900 input-output exemplar vectors 

• Cross-validation file, which includes a set of 150 input-output exemplar vectors 

• Testing file, which includes a set of 550 input-output exemplar vectors 

These files contain input vectors X(n) and output vectors D(n), each representing 15 

seconds of driving. 

Table 5.16: Number of exemplars in each data set (eye+steering) 

Number of Weights Training File Cross Validation Testing File 

322 900 150 550 

ANN TRAINING (EYE+STEERING) 

The training was performed in a similar way as explained earlier in the section on 

steering angle model. The same training parameters and cross validation techniques we re 

used as that in the steering only model. 

Table 5.17 shows the ANN training performance. Figure 5.7 is a graph of the Mean 

Square Error (MSE ) for training and cross validation of the network and indicates the 

network performance du ring training. The smooth drop of MSE in dicates successful 

training of the ANN. 

114
 
MSE versus Epoch 

0 

0.05 

0.1 

0.15 

0.2 

1 5 246 295 3 442 491 

Epoch 

0.25 

0.3 

0.35 

0.4 

0.45 

MSE

Training MSE 

Cross Validation MSE 

0 99 148 197 344 39 

 

Table 5.17: ANN performance during training (eye+steering) 

Best Networks Training Cross Validation 

Epoch # 500 500 

Minimum MSE 0.0433 0.0407 

Final MSE 0.0433 0.0407 

Figure 5.7: ANN training performance (eye+steering) 

ANN TESTING (EYE+S TEERING) 

During the training process, ANN was tested after each iteration to test its performance. 

Table 5.18 shows network testing performa nce for the best network weights. The test 

data set (test file) contained 190 input vectors, each representing 15 seconds of driving. 

The test data was not used in training or cross validation (i.e., the network had never se en 

this data before). 

Table 5.18: Test results (eye+steering) 

Output/Desired AWAKE DROWSY 

WAKE 246 34 

DROWSY 24 246 

% CORRECT 91 88 

115
 
The network correctly identified 246 out of a total of 280 drowsy intervals, an accuracy 

of 88%. There were 24 “false alarms” in which intervals were in the awake class but 

were misclassified as drowsy. Also, 34 intervals that were in the drowsy class were 

misclass ified by the network as awake. 

RESULTS AND DISCUSSION FOR TESTING ALL SUBJECTS 

Data from onl y eight subjects was used for training and initial testing. This data was 

mixed and ran domized and did not represent a clear picture of driver behavior through 

the whole driv ing session. In this section, the results of the ANN models for all tested 

drivers during both morning and night sessions are presented. These results show a 

continuous picture of driver behavior throughout the driving sessions. 

The data from both morning and night sessions was presented to the ANN for each of the 

driver. Overall, the performance of the drowsy driver detection systems was extremely 

good, as shown in Table 5.19. Both drowsy driver detection systems experienced som e 

false alarms, in w hich the ANN misclassified some of the awake data as drowsy. Both 

systems producedan acceptable accuracy rate of between 85 and 88%. 

Table 5.19: False Alarm Rate and Accuracy 

Steering Steering +Eye 

False Alarms 14% 9% 

Accuracy 85% 88% 

Figures 5.8 through 5.20 show ANN outputs for all the subjects. It should be noted, 

because of the problems associated with the current eye tracker system, discussed in Eye 

Closure Data, some portions of the eye data are discarded from the results. These sections 

are hatched on the figures. 

•	 The first graph is the area graph showing the fraction of time when the eye was 

closed in a 15 second time interval 

•	 The second bar graph is output of the ANN, based on the steering only model 

•	 The third bar graph is the output of the ANN, using both eye and steering angle 

data 

In Figure 5.8, the x-axis for the ANN output represents 15 seconds time intervals. Each 

red bar means the ANN is classifying that particular interval as drowsy while the rest are 

classified as awake. 

116
 
Morning (Subj. 2) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time thaclosed during a 15-second time interval. The secgraph shows the ANN output for the steering oncombined steering and eye model respectively. Erepresents a 15-second time interval that was cla‘Drowsy’ by the ANN. 

Night (Subj. 2) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 441 461 48

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 

time (sec) 

Figure 5.8: ANN output for subject 2 

117
 
Morning (Subj. 7) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time tclosed during a 15-second time interval. The segraph shows the ANN output for the steering ocombined steering and eye model respectivelyrepresents a 15-second time interval that was c‘Drowsy’ by the ANN. 

Night (Subj 7) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 

15 sec time interval 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 

time (sec) 

0 

1 

1 

Figure 5.9: ANN output for subject 7 

118
 
Morning (Subj. 8) 

0 

1 

1 21 41 61 81 101 121 141 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time tclosed during a 15-second time interval. The segraph shows the ANN output for the steering ocombined steering and eye model respectivelyrepresents a 15-second time interval that was c

‘Drowsy’ by the ANN. 

Night (Subj 8) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 441 461 481 501 521 5

15 sec time interval 

0 

0.2 0.4 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 800

time (sec) 

0 

1 

1 

Figure 5.10: ANN output for subject 8 

119
 
Morning (Subj. 9) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 9) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 

time (sec) 

Figure 5.11: ANN output for subject 9 

120
 
Morning (Subj. 10) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 10) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 40

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 600

time (sec) 

Figure 5.12: ANN output for subject 10 

121
 
Morning (Subj. 11) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time thaclosed during a 15-second time interval. The secgraph shows the ANN output for the steering oncombined steering and eye model respectively. Erepresents a 15-second time interval that was cla‘Drowsy’ by the ANN. 

Figure 5.13: ANN output for subject 11 

Night (Subj 11) 

0 

1 

1 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 34

15 sec time interval 

21 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 

time (sec) 

0 

1 

1 

122
 
Morning (Subj. 13) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.4 0.8 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 13) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 0.8 

1 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 

time (sec) 

Figure 5.14: ANN output for subject 13 

123
 
Morning (Subj. 14) 

0 

1 

1 21 41 61 81 101 121 141 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.4 0.8 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 14) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 0.8 

1 

1 501 1001 1501 2001 2501 3001 

time (sec) 

Figure 5.15: ANN output for subject 14 

124
 
Morning (Subj. 15) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.4 0.8 

1 501 1001 1501 2001 2501 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 15) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 441 461 481 501 52

15 sec time interval 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 

time (sec) 

0 

1 

1 

Figure 5.16: ANN output for subject 15 

125
 
Morning (Subj. 16) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 

1 501 1001 1501 2001 2501 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Figure 5.17: ANN output for subject 16 

Night (Subj 16) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 441 461 481 

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 

time (sec) 

126
 
Morning (Subj. 17) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 17) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381 401 421 441 461 481 501 52

15 sec time interval 

0 

0.2 0.4 0.6 0.8 

1 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 

time (sec) 

0 

1 

1 

Figure 5.18: ANN output for subject 17 

127
 
Morning (Subj. 19) 

0 

1 

1 21 41 61 81 101 121 141 161 

15 sec time interval 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 

1 501 1001 1501 2001 

Time (sec) 

` 

The first graph shows the fraction of the time that closed during a 15-second time interval. The secongraph shows the ANN output for the steering only combined steering and eye model respectively. Earepresents a 15-second time interval that was class‘Drowsy’ by the ANN. 

Night (Subj 19) 

0 

1 

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 3

15 sec time interval 

0 

1 

1 

0 

0.2 0.4 0.6 0.8 

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 

time (sec) 

Figure 5.19: ANN output for subject 19 

128
 
Morning (Sub 

41 61 81 101 

15 sec time interva 

j. 20) 

0 

1 

1 21 121 161 

l 

141 

Fraction of Eye Closure 

0 

1 

1 

Steering 

St + Eye 

0 

0.2 0.4 0.6 

1 501 1001 1501 2

Time (sec) 

001 

` 

The first graph shows the fraction of the time that 

closed during a 15-second timeinterval. The secongraph shows the ANN output f or the steering only combined steering and eye mod el respectively. Earepresents a 15-second time int erval that was class‘Drowsy’ by the ANN. 

Night (Subj 20) 

0 

1 

1 21 41 141 181 201 221 241 261 281 301 321 341 361 381 4 

15 sec time interval 

61 81 101 121 161 01 421 441 461 48

0 

1 

1 

0 

0.2 0.4 0.6 

1 5 001 01 3001 3501 4001 4501 5001 5501 6 

time (sec) 

01 1001 1501 2 25 001 6501 7001 

Figure 5.20: ANN output for subject 20 

129 
The previous figures show that during the morning session most of the time the ANN 

output consists of awake intervals. The ANN also output few false alarms (i.e., intervals 

in the morning session were classified as drowsy). During the night session, a much 

higher number of intervals were classified as drowsy. In general, this trend is true for 

both ANN models. 

There are very few false alarms in the steering only model output. On the average, only 

1.5% of the morning data was classified as drowsy, less than three for close to 50 miles of 

driving. This is in a range which is quite manageable. If the system is set such that the 

actual alarm will be issued only if there are two or more consecutive ‘Drowsy’ outputs, 

this problem can be completely eliminated without sacrificing the system detection 

performance. For the night session, 16% of the steering only ANN output was classified 

as drowsy, which is not equally spread along the whole session but rather clustered in 

certain regions. Figure 5.21 shows this classification for the steering only ANN model. 

% of data classified as Drowsy (Steering Only) 

10 

20 

30 

40 

50 

Morning 

Night 

0 

2 7 8 9 10 11 13 14 15 16 17 19 20 21 

Subject 

Figure 5.21: Percentage of ‘Drowsy’ intervals in morning versus night sessions 

(steering only) 

When eye closure data was added to the model, it played a dominant role as is clear from 

Figures 5.8 – 5.20. The output of the ANN model that includes both steering an d eye 

data shows a higher number of false alarms (i.e., intervals in the morning session that 

were classified as drowsy). Also, a much higher number of night session data was 

classified as drowsy. Figure 5.22 shows the percentage of ANN output that is classified 

as drowsy during both the morning and night sessions. On average, 12% of morning 

session and 42% of night session data was classified as drowsy. 

130
 
% of dataclassified as Drowsy(Steering +Eye) 

0 

20 

40 

60 

80 

100 

2 7 

Morning 

Night 

8 9 10 11 13 14 15 16 17 19 20 21 

Subject 

Figure 5.22: Percentage of ‘Drowsy’ in morning versus night sessions (steering+eye) 

As explained in the description of data collection in Chapter 4 of this report, there were 

problems with capturing eye closure data for some subjects. The eye tracking system 

was not able to properly track the pupil diameter due to the type of glasses subjects were 

wearing. Also, data from the night session of subject, 10 and 14 recorded unusual 

amount of eye closures due to system misalignment by the subjects when they scratc hed 

their face or rubbed their eyes. If this data is removed, the percentage of false alarms 

drops from 12% to 3% and the drowsy intervals for night session from 42% to 30%. 

These high percentages are a consequence of improper data recording or limitation of th e 

technology and not of efficiency of the ANN model. For subjects whose eye data was 

recorded properly, the ANN output was very similar to that of the steering model. 

Except for the subjects mentioned above, the output of both the ANN models correlates 

very well with the eye closure data. Figures 5.8 – 5.20 show that although there is no one 

to one correlation in all areas of high closure activity, the ANN output of both ANN 

models shows dense clusters of drowsy output every time there is a high intensity of eye 

closures. 

Figure 5.23 shows the average five minute PERCLOS measures for all drivers. Subjects 

whose eye data was properly recorded show an increase in average PERCLOS from 30% 

to 300% between morning and night sessions. Subjects 15 and 16 actually show a 

decrease in the average values of this measure but a look at the full data show short 

durations of high eye closures followed by long durations of low closures. The morning 

session was shorter than night session, which caused the average to fall. 

131
 
Figure 5.23: Average five-minute PERCLOS 

Figure 5.24 shows a comparison of PERCLOS and ANN output of the steering model for 

six subjects. There is no one to one correlation between the two but it can be clearly seen 

that a segment of high eye closure activity is either followed or preceded by clusters of 

drowsy warnings. 

In conclusion, the eye closures provide a good measure of driver drowsiness and can be 

used as a detection method. Systems used for eye closure monitoring do not work 

properly in all situations. The problems with the accurate detection of eye closures arise 

for a variety of reasons. It is highly dependent on individual behavior, face and eye 

structure, glasses, lighting conditions, and the system being used. It works for some 

individuals while completely failing for others. The technology is still not up to the task 

of detecting eye closures under all driving conditions unobtrusively. The eye tracking 

systems fails during the time when it is critically needed (i.e., when the driver is drowsy 

and is moving his head or rubbing his eyes). Unless these problems are solved, the model 

based only on eye closures will not be sufficient in itself. 

Eye closure data dominate the model output in a combined steering and eye closure 

system. It is because of this dominance that the combined model did not perform well for 

some drivers due to the shortcomings in an eye monitoring system but the steering model 

performed well in a majority of cases. 

132
 
Figure 5.24: Comparison of ANN output with PERCLOS measure 

133
 
CRASH PREDICTION RESULTS 

The ability of the drowsy driver detection system to predict crashes is perhaps the most 

important evaluation criterion. As indicated in the data analysis of the previous chapter, 

collisions are highly correlated with drowsiness. Thus, warnings issued before a collisio n 

can be seen as legitimate warnings as opposed to false alarms. Moreover, the primary 

purpose of the system is to prevent collisions arising from drowsiness. Thus, the most 

important assessment metric for a drowsiness detection system is the ability to issue a 

warning in a timely fashion be fore an acciden t is caused by driver fatigue. An analysis of 

the accidents in the simulation system indicates that this system is able to predict all of 

the initial crashes that o ccurred duringthe experiment . 

During the experiment, drivers fell asleep w hile driving and crashed the vehicle by 

colliding with other vehicles, departing the roadway, or running into a barrier. In Figures 

5.8 – 5.20, these crashes are indicated by ablack line. There were a total of 130 crashes 

recorded. 

To fu rther invest igate the system detection performance during the early stages of 

drowsiness (i.e., at drowsiness onset), the research team looked at the 5 minutes 

preceding the two first crashes of each driver. Only the first two crashes were examined, 

because the driver may remain drowsy through multiple crashes. Thus, they may be little 

or no steering activity in between sequential later crashes. In reality, the driver would be 

unlikely to continue driving after experiencing one crash. However, to expand the 

sample size of the data, the first two crashes were considered. 

When the detection system is coupled with a warning system, detailed analysis must be 

done to evaluate the extent to which fatigued drivers may use the warning system to 

enable them to continue driving. This concern, as well as other issues that arise with a 

warning system, is discussed in detail in the next chapter. 

An evaluation of the steering-only ANN shows very good performance under the crash 

prediction metric. The system issued a warning in a timely fashion for 100% of the first 

two crashes experienced by any of the subjects. For all of these crashes, the model based 

on steering data only output at least one warning in the 5 minutes preceding the crash. On 

average, 5.6 warnings were issued during the 5 minutes preceding the crash. The first 

warning was issued on average 3 minutes and 56 seconds prior to the crash and the final 

warning was issued on average 16 seconds before the crash. 

SUMMARY AND COMPARISON WITH PREVIOUS DATA 

In conclusion, the performance of the drowsy driver detection systems for truck drivers 

was acceptable. The performance of the system was similar to previous systems that 

detected drowsiness in car drivers. The truck driver fatigue detection systems performed 

extremely well in the most important evaluation metric, timely crash prediction. 

134
 
Results of the car simulator study are provided in Appendix A. A comparison of the two 

studies show s that the results are very similar. The two experimental conditions were not 

the same an d differed in the type of simulator an d driver (subject) population, although 

the amount of sleep deprivation were the same. Table 5.20 shows the results for both of 

these studies . The percentage accuracy during training decreased slightly for the truck 

study. The number of false alarms increased slightly for the truck study. 

Table 5.20: Performance comparison of tru ck and car fatig ue detection systems 

Truck Study Car Study 

Steering Steering+Eye Steering Steering+Eye 

False Al arms 14% 9% 13% 7% 

Accuracy 85% 88% 86% 92% 

Crash Prediction 

(1st ashcr ) 100% 100% 100% 100%

Table 5.20 also shows that the syst em was able to predict 100% of the first crashes for all 

of th e subjects. (Note: After the f irs t crash in the sim ulator, driver s continue driving and 

subsequent crashes were possible.) Moreover, these collisions were detected in a timely 

fashion withan average of 5.6 warnings issued in the 5 minutes prior to a collision. The 

first of these warnings was issued on average, 3 minutes and 56 seconds prior to the 

crash. Consequently, this detection system holds thepromise of safer roadways when 

coupled with a warning system. 

The steering wheel signal was also visually observed to detect any signal degradation 

prior to the first crash for eachsubject. Table 5. 21 shows the comparison of PERCLOS, 

steering wheel angle signal observation, and AN N st eering detection algorithm results for 

each subject. The subjects showed signal degradation anywhere from 12 to 1 minute prior 

to their first crashes. Moreover, ten ou t of eleven drivers, who experienced crashes, had 

steady or flatten steering signal (phase 2) phenomen on right before crashes. 

135
 
Table 5.21: Summary of Steering Angle Sig nal Data Obser vation and ANN Results for the 1s

Subject 

Data/Experiment O servation b ANN 

First 

Crash 

Comm 

E 

ent on 

ey 

PERCLOS eeriSt ng Time of d

( 

Time 

Min:Sec) 

a 

Any 

Off o 

min b 

cr 

Doze 

ver 3 

efore 

sh 

1 min 

before 

crash 

3 min 

before 

crash 

5 min 

before 

crash 

signal 

degradation 

detectable by 

human 

inspection 

C P 

Stead 

rash 

y at 

oint 

M 

( 

in before 

crash 

Min:Sec) 

Farth

warn

(Min:

2 No Crash 7 36:47 eY s N/A N/A N/A Yes Steady 7:15 3:48 148:36 eY s 20% 18% 16% Yes Steady 5:00 2:1

9 87:45 eY s 64% 42% 41% Yes Steady 7:00 8:0

10 51:31 eY s 44% 44% 43% Yes Steady 12:00 6:2

11 65:33 eY s 31% 33% 35% Yes Steady 3:30 3:3

13 38:55 eY s 12% 12% 12% Yes Steady 9:00 2:1

14 12:00 eY s 85% 88% 86% Yes Steady 1:20 1:0

15 No Crash 16 137:44 eY s 6% 3% 4% Yes N te ot S ady 3:00 3:4

17 103:47 eY s 28% 40% 32% Yes Steady 10:00 8:4

19 37:20 esY 33% 39% 39% Yes Steady 8:00 2:1

20 110:46 esY 53% 41% 37% Yes Steady 15:00 2:3

Average 34% 32% 31% 6:36 4:0

136
 
 

6. DRIVER WARNING 

The drowsiness detection system analyzes data for a specific time interval and then 

classifies that interval as wake or drowsy (in previous experiments the onset of crash was 

considered a definite drowsy state). However, when and how should a system warn a 

driver about the hazardous situation is another area of driver/human factors research? T o 

effectively inform the driver of his drowsiness state and prevent him from potentially 

hazardous driving situations, a driver’s assistance system needs to be coupled with the 

drowsiness detection system and integrated in the vehicle cabin. The design and 

integration of a warning system is directly related and dependent on the warning 

algorithm. The timing, intensity, and nature of the warning system to be integrated with 

the detection algorithm are described. This effort builds upon the existing data and 

knowledge of FMCSA in this field. This research describes the challenges involved in 

such a warning system. Specifically, this chapter: 

• Explores different aspects of a driver alarm system 

• Explores all the available and under development systems 

• Defines design guidelines for a driver drowsiness warning system 

Although extensive research has already focused on warning systems for collision 

avoidance, lane departure avoidance, and speed adaptation, warning systems for driver 

drowsiness mitigation have received less attention. Given the human factors issues and 

technical challenges involved in this type of warning systems, more research is needed t o 

address the remaining efficiency challenges and potential drivers’ acceptance issues. 

Different levels of persistence and intervention have been identified for in-vehicle 

drivers’ assistance sy stems. Persistence can range from voluntary systems, which can be 

disabled by the driver, to mandato ry systems, which are always on. Devices can vary by 

their level of intervention and includ e advisory, warning, recording, and automatic 

control systems. Advisory systems provide information to the driver such as his/her 

current drowsiness state. Warning systems provide alarms when the system determines 

that immediate driver action is needed to avoid a potentially hazardous situation. 

Recording systems record drivers’ behavi or and driving performance information for 

review by drivers, fleet operators, vehicle owners, or law enforcement authorities. 

Systems with automatic control intervention can, for example, use braking forces control 

to slow the vehicle down and eventually bring it to stop. 

For driver drowsiness applications, unless the system knows the vehicle position, 

orientation, and kinematics in real-time during driving, the system should not take control 

of the vehicle. A drowsiness assistance system should therefore be either an advisory or 

warning system, whose role is to redirect drowsy drivers’ attention to the driving task, or 

a recording system for post-driving evaluation of the drivers’ fatigue level during their 

shifts. 

137
 
WARNING SYSTEMS CHALLENGES 

The main role of in-vehicle warning systems for driver drowsiness mitigation is to 

redirect drowsy drivers’ attentio n to the driving task. Because unexpected effects due to 

a sudden warning alarm can cau se undesired drivers’ reaction and lead to risky driving 

situations, the warning system should be capable of breaking driver’s drowsiness in a n 

effective manner without startling the driver (Vallet et al, 1993; Haworth and Vulcan, 

1991). 

In the interaction between the driver and the automated warning system, both parties are 

capable of error. A warning system should be able to generate response for optimum 

system effectiveness and driver interaction. The following considerations need to be 

adequately addressed if the system is to achieve effective safety improvements: system 

effectiveness, user acceptanceand trust, issuance of false alarms, training issues, and 

issues of warning understanding. 

System Effectiveness 

Various measures related to the driver’s drowsiness could be used to evaluate the 

effectiveness of a warning system. A comparison of driving performance (e.g., the 

number of crashes or lane departures av oided, standard deviation of the vehicle lateral 

position, etc.) and driver’s physical and physiological variables (e.g., driver’s eye 

activity, EEG, heart rate, etc.) with and without the warning system can help evaluate the 

warning system effectiveness for driver’s drowsiness mitigation. However, accurate 

evaluation of a driver drowsiness warning system would require testing and comparing 

such variables under identical levels of driver drowsiness. Since a perfect measure for 

drowsiness is not available, some variability during the testing of the system efficiency 

may be introduced. 

User Acceptance and Trust 

Previous research has identified driver’s trust in automated in-vehicle warning systems as 

a critical factor determining system effectiveness. User interaction with the system is, 

therefore, highly determined by his level of trust in the system (Itoh et al, 1999; Lee et al, 

1998; Lee and Moray 1992; Muir 1994). 

Driver reactions to a warning system can vary significantly for different drivers. An ideal 

drowsiness warning systemshould be able to take individual differences into account 

when generating a signal. This adaptability of the system to drivers’ reaction and 

preferences can, for example, be envisaged by graded levels in the warning signal. 

Issues of drivers ignoring warnings have also been reported in the literature. Uaneras et 

al (2005) indicated that one third of subjects responding to alarms from a back-up 

warning system heard the warning, but decided to ignore it because they did not perceive 

any imminent danger. 

138
 
User acceptance can be determined both by subjective questionnaires addressed to the 

drivers or by monitoring drivers’ use of the system. Drivers, who reject or are annoyed 

by the system, may ignore the system warning signals or simply switch the system o ff. 

Issuance of False Alarms 

The initial consideration for setting the decision threshold of an automated warning 

system is the cost of a false negative (missed alarms) versus that of a false positive alarm 

(Parasuraman et al, 1997). 

False positives 

False alarms can lead to users’ decreased trust in the system and delayed response. Gupta 

et al (2002) showed that drivers trust low sensitivity warning systems more than high 

sensitivity warning systems. 

Enriquez and MacLean (2004) showed tha t false positives alarms have a deleterious 

effect on driver’s ability to interact effectively with a haptic warning system. Excessive 

number of false positives affects driver’s trust in the system. False negatives (missed 

alarms) showed no influence on driver’s ability to use the warning signal. Bliss and 

Acton (2003) also observed that alarm reliability level significantly affects drivers’ 

reaction to a collision warning system. 

Other studies have shown that the number of false alarms is not necessaril y detrimental to 

the system/driver performance up to some point. Dingus et al (1997) observed that 

distrust in a collision warning system due to an excessiv e number of false alarms occurs 

when a 60% rate of false alarm is reached. Although they mentioned that drivers were 

somewhat affected by the false alarms, Ben-Yaacov et al (2002) found similar results. 

Both concluded that the warning system does not necessarily need to be perfect for th e 

driver to adopt safer driving strategies. 

CISR believes that an excessive number of false alarms are not as problematic for 

drowsiness warning system as for other types of driver’s assistance systems. Where a 

false collision warning alarm can lead to a driver’s sudden braking and create a hazardo us 

driving situation, a false drowsiness warning would not trigger any inappropriate reactio n 

from an alert driv er. However, if drowsiness false alarms are not safety critical, they can 

probably contribute to driver’s annoyance if they are too frequent and may lead the driver 

to switch the system off. 

False negatives 

Missed alarms in the driver assistance system – like a drowsy driver warning system – 

can lead to hazardous driving situations and most probably to crash. It is, therefore, 

safety critical that the number of missed alarms nears zero. A com promise between the 

number of false positive and false negative alarms needs to be found when assessing the 

warning system sensitivity. 

139
 
Training Issues 

Proper training for a warning method can help drivers to better react to the warning 

signals. Adequate mental processing of a warning can be significantly improved by 

training. Drivers’ decisions and actions following a warning signal can be sped up and 

automated if they have previously learned how to handle the signal information. 

The benefits gained from training may depend on individual differences. For example, 

working memory capacity for memorizing warning icons has been shown to differ 

significantly between drivers of different age groups (Lesch, 2003). Although this study 

only investigated subjects’ capacity to understand and memorize visual warning symbols , 

if such results should prove to be extendable to other types of warning signals, the issue 

of individual driver differences in analyzing a warning signal should be considered when 

designing a warning system for driver drowsiness. 

Issues of Warning Understanding 

Ease of warning meaning recognition is also important (Suzuki and Jansson, 20 03). 

Drivers’ mental model for responding to the warning stimulus should effectively interpret 

the real conditions of the vehicle. If this is achieved, the drivers will respond in a correc t 

manner to the warning. Otherwise, drivers can misunderstand the warning and adopt an 

even riskier behavior. Meredith and Edworthy (1995) showed that ambiguous meaning 

of alerts could undermine systems performance and increase workload. 

WARNING STRATEGIES 

Various strategies can be envisaged for alerting the driver of his/her drowsiness state . 

The following questions should guide the design of an effective drowsiness warnin g 

system: 

•	 What information to provide to the drowsy driver (i.e., nature and content of t he 

warning message)? 

•	 When to provide this information to the driver (i.e., timing and sequence of the 

alert)? 

•	 How to convey this information to the driver (i.e., modality of the warning 

signal)?
 

Warning Content 

A warning must attract the driver’s attention, convey the correct message, and suggest 

the corrective action to be taken. (Bekiaris and Nikolaou, 2004) 

A driver drowsiness warning system should be able to present accurate information to the 

driver about his/her drowsiness level. Excessive discrepancies between the driver’s 

understanding of the alarm and the actual driving situations may impair the system 

effectiveness. In his urgency mapping principle, Edworthy (1994) advocates that the 

urgency of the situation should match the perceived urgency of the alerts. 

140
 
A simple drowsiness warning system should be limited to informing the driver abo ut 

his/her status. More advanced systems tha t include situation awareness capabilities can 

possibly advise the driver of the appropriate corrective action to take given the cur rent 

driving conditions. Such driver support systems would consider the vehicle kinematics 

parameters and surrounding environment to work in a situation-specific manner (Onken, 

1994). 

Ideally, drivers should not use the warning system as a way to drive longer when 

fatigued, but should understand from the message that they must bring the vehicle to a 

stop to get some rest. The warning content should be clear enough that no driver 

misinterpretation is possible, but should also be kept simple to avoid excessive attent ion 

from drivers. 

Type of Warning 

Driver sensitivity to warning signals can be very different. Some drivers may understa nd 

a warning immediately and, therefore, be very quickly annoyed by repeated warnings, 

while other drivers may need to be warned several tim es before they respond. 

Graded warning, as compared with single-stage warning, can be a simple way to adapt 

warning to individual differences. A system able to account for the drivers’ response to a 

warning would be suitable for all types of drivers. Such a system would stop alerting 

highly sensitive drivers early because they already responded to the warning, while 

continuing to alert less sensitive drivers until they respond to the warning. Horowitz et al 

(1992) also underlined the importance of warning adaptability to individual behaviors. 

However, the authors believe that the adjustment of the warning frequency by the drive r 

himself, needs further analysis as drivers may not necessarily be able to objectively 

determine the warning setting that can optimize their interaction with the system. 

Graded warning was also shown to improve driver’s understanding of warning and was 

preferred by drivers over single warning, at least for visual displays (McGehee et al, 

1994). Gupta et al (2002) reported that graded warnings of an imminent driving hazard 

based on urgency of the situation resulted in imp roved drivers’ vehicle control. 

Bekiaris and Nikolaou (2004) recommend two levels of warning for cautionary and 

imminent cases. Cautionary warning should be generated in the case of a hazardous 

driving condition requiring no immediate action from the driver. Imminent warning 

should be triggered when immediate response from the driver is necessary to avoid a 

crash. In a comprehensive literature overview of in-vehicle warning systems, NHTSA 

recommends variable-intensity warning displays for driver drowsiness warning 

(COMSIS, 1993a, b). 

Warning Timing 

The determination of the alarm timing is crucial for the alarm effectiveness (Janssen and 

Nilsson, 1993). A poorly timed warning may undermine driver safety (McGehee et al, 

2002). An alert issued too early may be ignored by drivers if they are unable to perceive 

141
 
the cause of warning, and an alert issued too late may be ineffective or disrupt the 

ongoing driver’s action (Lee et al, 2002). 

Drivers may judge whether an alarm is trustworthy or not according to its timing 

independent of its validity (Abe and Richardson, 2004). Early timing can be associated 

with nuisance and false alarms. This can influence driver perception of alarm validity 

and reliability with consequent effects on system acceptance and effectiveness. 

If a warning is issued early or just after driver’s response has been initiated, it acts to 

speed up the driver’s response. However, if the alert signal is issued during the driver’s 

response planning or when he/she is in the middle of his response maneuver, it may 

interfere with the response decision or execution and can be viewed as unhelpful and 

annoying (Brown et al, 2001, McGehee et al, 2002). Late alarms can also lead to 

decreased drivers’ trust in the warning and, therefore, degrade warning effectiveness. 

Alarm timing, like the type of warning, ideally would adapt to individual drivers. For 

example, when defining an alarm’s timing, drivers’ behavior inputs (i.e., reaction time) 

should ideally be taken into account (Kiefer, 2000). 

Warning Modality 

Independent of the warning mode used, the warning signal should be capable of 

overcoming sleep inertia but should not induce a startled reaction of the driver (Tepas and 

Paley, 1992). Various modes of warning have already been studied for in-vehicle 

driver’s assistance systems like collision and lane departure avoidance or speed 

adaptation systems. All methods of warning have been proven to be effective to some 

degree depending on their intended application. However, an objective evaluation and 

comparative effectiveness of these solutions for driver drowsiness warning is not yet 

available. 

Existingwarning solutions can be classified in the following categories: visual warnings, 

auditory warnings, and haptic warnin gs. For multi-modal w arning systems, the warning 

sequence must also be taken into con sideration. 

Visual Warnings 

Visual information can b e displayed by in-vehicle m onitors, head-up-displays, icons o n 

instrument panel and rear and side view mirrors, et c. Various intensitie s, colors, and 

warning mess ages and signs can be envisaged. However, visual display is not an 

appropriate warning mode t o alert a drowsy driver, who may sleep through the warning. 

In addition, since driving is primarily a visual base d effort, visual displays may draw the 

driver’s attention away from the driving task more than other warning modes. 

However, visual displays have proven effectiveness in providing the driver with detailed 

information about the driving situation (e.g., head way distance, time-to-collision, vehicle 

lateral position in lane, etc). This type of warning could be used as a secondary warning 

to help the driver take the adequate driving actions, once he/she has been alerted by a 

primary warning. 

142
 
Auditory Warnings 

Tone and voice/speech messages can be used to conve y information to the drivers about 

an imminent driving hazard. Tones, beeps, rumble strip sounds, etc. were shown to be 

effective in attracting drivers’ attention and enabling them to quickly respond to crit ical 

situations, where immediate response is needed (Hirst and Graham, 199 7; Ross et al, 

1996; Horowitz et al, 1992). However, they can not convey detailed information to 

drivers about the hazardous driving situation. 

Different intensities of acoustic stimulus ma y be needed to arouse drivers from sl eep. An 

ideal drowsiness warning system should accom modate thesedifferences either by proper 

calibration to each individual driver be fore using the system or through a learning 

capability that automatically adjusts toeach driver’s sensitivity to alarms. Bekiaris and 

Nikolaou (2004) highlighted the need for a drowsiness warning system to be adjustable in 

presence, intensity, and type of warning to individual drivers’ prefere nces. Green et al 

(1995) recommends that an auditory warning be 15 decibels above the masked threshold 

of the vehicle cabin sound environment. A maximum absolute intensity of 115 db was 

suggested as intense sounds can induce drivers’ annoyance and workload. Ross et al 

(1996) suggested that no more than three tones be generated inside the vehicle to avoid 

driver confusion. 

Speech and voice messages can be used as secondary warnings to provide alerted drivers 

with more insight about the driving conditions an d actions to be taken. Speech messages 

should be short andtheir meaning obvious to avoid excessive attentional demand. Fahey 

et al (1995) found that a speaking rate of 150 words/minute was appropriate for speech 

messages. 

Lloyd et al (1999) identified the advantages and disadvantages of different tone and voice 

warning modes, as shown in Table 6.1. 

Table 6.1: Tone and voice warning messages from Lloyd et al (1999) 

Warning Tone 

• Omni-directional; orienting stimulus 

• Under normal conditions, demand less 

attention than voice 

• Processed faster than visual stimuli (Weber et 

al, 1994) 

• Language independent 

• Auditory icons which match a driver’s mental 

model produce faster, more appropriate 

responses (Graham and Mitchell, 1994) 

• Difficult to accommodate 

hearing-impaired drivers 

• Unable to conve 

information 

• Signal detection problem unde 

high ambient noise conditions 

• Could cause unwanted startled 

responses 

•

y detailed 

r 

Integration with other invehicle devices could lead to 

warning cacophony 

143
 
Voice Warning 

• Omni-directional; orienting stimulus 

• Processed faster than visual stimuli 

(Weber et al, 1994) 

• Able to convey detailed information 

• Speech may be more effective in high 

stress situations because speech meaning 

is over-learned (Edman, 1982) 

• Difficult to accommodate hearingimpaired drivers 

• Language dependent 

ces 

essages 

• May demand more attention than tone 

• Signal detection problem under high 

ambient noise conditions 

• Could cause unwanted startled 

responses 

• Annoying if unnecessary 

• Integration with other in-vehicle devi 

could distract the driver with abundance 

of verbal m 

Lee and Patterson (1993) indicated that spatial auditory warnings can help drivers locate 

an imminent danger and, consequently, enhance their corrective response. Spatialized 

sound alerts have also been shown to speed up a driver’s reaction to an imminent driving 

hazard, (Hoshino et al, 2002). However, the effectiveness of specialized sound warnings 

for drowsy drivers has not yet been evaluated. 

Haptic Warnings 

Existing literature indicated that haptic displays produce less mental workload on the 

driver and possibly shorter reaction times. Vibrations in the driver’s sea t, seat belt 

(Bekiaris and Nikolaou 2004), steering column (Naab and Reichart, 1994; Isomoto et al, 

1995), throttle pedal (Janssen et al, 1994), or brake pedal (Lloyd et al, 1999) can be used 

to alert the driver of an imminent driving hazard. Previous research has consistently 

reported the potential for haptic feedback to provide information to drivers that enh ances 

performance (Payandeh et al, 2002; Steele et al, 2001). 

Lerner et al (1996) found high drivers’ acceptance levels and high effectivenes s rates of a 

safety belt vibration system for drowsy driver warnings. A vibration frequency between 

100 and 300 Hz was found to be adequate for optimum driver’s response. 

Lloyd et al (1999) mentioned that haptic cues present many advantages as they are 

quickly perceived by drivers – hence, reducing their reaction time – and can be used t o 

cause an oriented response of the drivers if the stimulus is sufficiently intense. To 

achieve proper and fast reaction of the drivers to the haptic warning, Lloyd states that the 

warning should be transmitted through the same device (i.e., pedal, steering wheel) th at 

drivers would use to respond to the hazardous situation. 

Although they found that warning modality had little effect on driver perform ance, Lee et 

al (2004) reported that haptic warnings were preferred by drivers to auditory warnings on 

several dimensions including trust, overall benefit to driving, and annoyance. 

144
 
The main disadvantage of haptic warnings is that they are unable to convey detailed 

information to drivers. Drivers alerted by a haptic warning could benefit from a 

secondary warning (e.g., speech message) to better inform them of the current driving 

situations and to advise them of the correct actions to take. 

Warning Sequence 

Depending on the goal of the display, the optimum warning modality can be different . 

Previous research agreed on the safety potential of generating two stages of alert for 

drowsy driver warning. Auditory and haptic displays are probably more advantageous in 

imminent warning situations in which the driver is drowsy. This type of warning will 

redirect his attention to the driving task. A second stage of warning can then be 

generated to inform the driver that the alarm rel ates to his drowsiness state or provide 

him/her with information on current driving con ditions. Speech messages could be used 

for this purpose (Bekiaris and Nikolaou, 2004). 

Bekiaris and Nikolaou also underlined the need for maintaining drivers’ alertness after 

warning. Various perspectives can be envisaged here: 

•	 The warning system could be supplemented by a driver’s stimulating system (e.g., 

providing the driver with blasts of cold air, release of various scents, for exam ple 

peppermint, smell of coffee, etc.). 

•	 The warning should be clear enough that the driver understands that he/ she should 

pull over and get some rest. A system that would allow drivers to drive longer in 

advanced fatigue states would create a higher volume of drowsiness occurrences 

and, therefore, fail in meeting any safety enhancing objectives. 

•	 The warning system could communicate with the outside world and send a sign al 

to the monitoring center of a commercial fleet, or call a preset phone nu mber or 

the police, or flash lights and blow horn to inform the others of the danger. 

REMAINING ISSUES AND RE SEARCH OPPORTUNITIES 

Driver Behavioral Adaptations 

The effect of drivers’ behavioral adaptations after extended exposure to the system 

should also be considered before a drowsiness warning system is integrated in a real 

vehicle. Adverse effects can potentially be experienced including: 

•	 Drivers rely too much on the system and drive in unusually advanced fatigue 

states thinking that the system would alert them if necessary 

•	 Drivers get used to the system and pay less attention to the warnings 

•	 Drivers persevere in driving after a warning and are susceptible to get drowsy 

again (Haworth and Vulcan, 1991) 

145
 
Previous studies on vigilance and information acquisition capability proved th at subjects’ 

acquisition rates can decline significantly after few minutes on task. Information 

detection can fluctuate over time independently of subjects’ alertness/drowsiness level 

(Makeig and Inlow, 1993). 

Public Attitudes 

Other issues will also have to be addressed before wide commercial availability of a 

drowsiness warning system including public attitudes towards the system: willingness to 

pay for a drowsiness warning system, levels of effective use of the system in the case tha t 

drivers have the option of switching the system off, etc. 

Interaction with Other Safety Systems 

As other in-vehicle driver’s assistance systems are also being developed and will soon be 

available in real vehicles, potential issues of interference between different systems will 

need to be analyzed. For example, in the case of a drowsy driver drifting out of his 

traveling lane, alerts from both a lane departure warning system and a drowsiness 

warning system could be triggered simultaneously. This could cause a driver’s 

misunderstanding or confusion and prevent him from taking the appropriate driving 

action. Finally, an assessment of effective reduction of crashes related to drivers’ 

drowsiness would be needed to objectively evaluate the system effectiveness. 

Evaluation of a Driver Drowsiness Warning System in a Driving 

Simulator 

Experimental tests should be conducted before a driver drowsiness warning system can 

be widely deployed. The CISR truck driving simulator could be used to evaluate the 

benefits of a drowsiness warning system: overall system effectiveness, driver 

acceptance, driver behavioral adaptation to the developed system, evolution of driver’s 

driving and resting strategies and other safety enhancing effects, etc. Subjective and 

objective evaluation techniques could be used to study the performance of a new system . 

Comparative evaluation of various warning systems for drowsiness mitigation could also 

be envisaged. 

146
 
7. CONCLUSIONS AND FUTURE WORK 

This report described the successful tests of a truck driver drowsiness detection system . 

The hypothesis underlying this system is that fatigued drivers exhibit different steering 

patterns than non-drowsy drivers. Previous tests of this system demonstrated the 

effectiveness of this system for car drivers. Differences in car and truck driving, 

including the professional experience of truck drivers, the different dynamics of truc ks, 

and the different feel of steering were amply apparent in the setup of a truck driving 

simulator laboratory. However, this project demonstrated that these differences have a 

minimal impact on the performance of the drowsy driver detection system. 

After conducting a literature review of relevant studies in fatigue, drowsy driver detection 

systems, and vehicle-based warning systems, a truck driving simulator was constru cted 

for the experiments that assessed the effectiveness of the drowsy driver detectio n system. 

The fixed base simulator included a 135 degree field of view forward projection system; 

projection systems for both side mirrors; a realistic sound system; realistic feels for th e 

brake, acceleration, and clutch pedals; a gearbox; and a complete data recording system 

including full range of control inputs using optical encoders, vehicle kinematics, eye 

monitoring, and video recording from multiple camera angles. The simulator was 

validated by both truck drivers and experts in truck simulators. 

Once the simulator had been constructed and validated, the drowsy driver experiment 

was conducted. Fourteen subjects, who were truck drivers with valid commercial drivers 

licenses, participated in the experiment which consisted of three driving sessions for each 

subject. First, the subjects participated in a practice session, which lasted approximately 

one hour, in which they became accustomed to driving the simulator. Sometime later, 

usually the next day, the subject wou ld then participate in a one-hour morning session, 

which was held between 8:30 and 9:30 am. The purpose of the morning session was to 

establish a baseline of predominantly non-drowsy steering behavior for each subject. 

During the day, the subject was instructed to avoid napping and excessive caffeine 

consumption. That night, a member of the research team would drive the subject from 

their abode to the simulator. The subject would then participate in night driving session 

starting around 1:30 am, with each session lasting for approximately 2 hours. 

The data generated from these experiments was analyzed thoroughly to evaluate inputs 

for a drowsy driver detection system and performance m etrics for the system. This 

analysis showed a correlation between high amplitude steering corrections and 

drowsiness. This analysis also indicated that the crashes are strongly correlated with 

drowsiness. Due to this correlation and the primary purpose of the drowsy driver 

warning systems, collision avoidance, the ability of the drowsiness detection system to 

detect drowsiness in a timely fashion prior to a collision was established as the most 

important evaluation criterion. 

Although there was a strong correlation between drowsiness an d steering activity, the 

relationship between these two variables is complex and non-linear, and varies from 

driver to driver. This complexity and variability precluded the development of a prec ise 

model to detect driver drowsiness based on steering activity. On the other hand, 

147
 
 

Artificial Neural Networks (ANNs), with their ability to learn these types of 

relationships, are a natural approach for this type of system. 

Central to the success of an ANN is a data preprocessing stage in which the data are 

transformed to maximize the ability of the ANN to learn the desired relationship. The 

drowsy driver detection system developed in this project uses a unique data preprocessing 

step that discretizes the steering activity in fifteen-second intervals. This discretization 

enables the ANN to distinguish between low amplitude steering corrections in nondrowsy driving and the larger amplitude steering corrections that characterize drowsy 

driving. 

The performance of both the steering-only and steering-with-eye-tracking ANNs was 

similar to previous systems that detected drowsiness in car drivers, despite the differences 

in the experimental conditions for the two studies. These differences included the 

amount of sleep deprivation, the type of simulator, and the driver population. 

The effectiveness of the drowsy driver detection algorithm provides evidence that it 

would be an important component of a drowsy driver prevention system. This system 

would incorporate countermeasures, for example, warning systems that minimize the 

severity of crashes and reduce injuries and fatalities in highway crashes involving trucks. 

This report presented design guidelines for such a warning system. The timing, intensity, 

and nature of the warning system to be integrated with the detection algorithm were 

discussed. 

This project lays the foundation for future work in the development and deployment of a 

drowsy driver detection system. The primary areas for future work are the development 

and testing of prototype detection systems and the integration of a warning system into 

the drowsy driver detection system. 

Given the success of the drowsy driver detection system in the simulator experiments, the 

next step in the evaluation of the system is the development of a prototype system that 

can be deployed in an actual vehicle. The prototype system will include both sensors and 

a computing system. Since the system relies only on the steering signal for input, the 

sensor requirements are minimal. This prototype system would then be used to evaluate 

the system effectiveness, first in track tests, and then in field operational tests. The 

purpose of these tests is to evaluate the effectiveness of the detection system, rather than 

a combination of a detection and warning system. 

Central to the success of the drowsy driver detection system is the preprocessing step that 

discretizes the steering signal. Since this algorithm has proven relatively successful on 

three sets of simulation data with three different steering sensitivities, it is likely that this 

preprocessing step will enable the detection system to perform well in real word tests. 

Currently, the ranges in the discretization step are determined offline. When creating the 

prototype system, one future challenge will be implementing the algorithm online so that 

the ranges for the discretization are determined automatically. 

Based on the design guidelines developed in Chapter 6, the warning interface for the 

drowsy driver detection system should be developed. Simulator tests should be 

148
 
conducted to evaluate the most effective warning modality, timing sequence, and wa rning 

level for safely and effectively warning drowsy drivers. After these initial simulator ­

based tests, track tests and field operational tests would then be needed to fully evaluate 

the warning systems. 

149
 
APPENDIX A. RESULTS FROM PREVIOUS ANN 

MODELS 

CISR conducted driving simulator experiments using drivers under sleep depriv ed 

conditions to collect data. This data was used to develop models for detecting drowsy 

driving. Results of this study are presented here. The data preprocessing technique and 

the training and testing parameters were the same as those described in Chapter 4. 

EXPERIMENTAL PROTOCOL 

The experiment was conducted in the CISR car driving simulator laboratory. Twelve 

subjects with age ranging from 22 to 42 years participated in the study. Most were 

recruited from the GWU student population. During the experiment, subjects drove the 

car simulator in a rural highway driving scenario two times. Table A.1 shows the 

schedule and amount of sleep deprivation for the two sessions. 

Table A.1: Amount of Sleep Deprivation 

Session I Session II 

Amount of Continu 

Wakefulness

ous 1 – 2 hours 18 – 21 hours

Time Schedule 8:30 am – 10 am 1:00 am – 3:00 am 

Data colleted during the experiment was preprocessed and used to train an Artificial 

Neural Network for detecting steering behavior representing drowsy driving. Data 

processing techniques are the same as those described in Chapter 4 of this repo rt. 

ANN MODELS 

Two ANN models were developed. Each was trained and tested using different type of 

data – a steering only model anda steering plus eye m odel. Training, testing, and results 

of these models are discussed here. 

ANN TRAINING (STEERING) 

The shortest experimental sessio n for which data was recorded was 20 minutes (i.e., 

eighty 15 second time intervals). To balance the data, only the first 80 intervals from the 

morning session and the last 80 in tervals from the night session were used. Data from 10 

subjects, comprising a total of 1,600 driving intervals, was randomized and then divided 

into three separate files:
 

150
 
• Training file, which includes a set of 750 input-output exemplar vectors 

• Cross validation file, which includes a set of 100 input-output exemplar vect ors 

• Testing file, which includes a set of 750 input-output exemplar vectors 

These files contain data in the form of input vectors X(n) and output vectors D(n), each 

representing 15 seconds of driving. The ANN performance during training is shown in 

Figure A.1. The smooth drop in MSE shows that the ANN performed very well. 

MSE versus Epoch 

Epoch 

E 

Training MSE Cross Validation MSE 

0.25 

0.15 

0.2 

0.05 

MS 

0.1 

0 

1 251 501 751 1001 1251

Figure A.1: ANN training performance 

ANN TESTING (STEERING) 

Table A.2 shows ANN performance during testing. The ANN correctly identified 318 

out of a total of 371 drowsy intervals (86% accuracy) while 53 intervals were 

misclassified. The ANN also correctly identified 332 wake intervals out of a total of 379 

wake intervals (88% accuracy). 

Table A.2: ANN test results (steering) 

OUT PUT 

DESIRED 

WAKE DROWSY 

WAKE 332 53 

DROWSY 47 318 

% CORRECT 88 86 

151
 
CRASH PREDICTION 

During the experiment, drivers fell asleep while driving and crashed the vehicle by 

colliding with another vehicle or with a barrier. The network identified sleep patterns 

before the crash in all the cases (i.e., an accuracy of 100 percent). The first bar graph in 

Figures A.2 – A.5 is the ANN (steering angle only) output for all subjects. It can be seen 

that the network predicted drowsy driving before the crash occurred in all the cases. 

TESTING ALL SUBJECTS 

Figures A.2 – A.5 show ANN output of CISR data for all the subjects. 

• The first bar graph is output of the ANN based on the steering-only data model. 

• The second bar graph shows driver eye activity. Each bar represents the fraction 

of time when the eye was closed in that particular 15 second time interval. 

•	 The third bar graph is the output of the ANN, using both eye and steering angle 

data. 

Figures A.2 – A.5 show the ANN output, for both morning and night driving sessions, of 

each subject. Observation of these figures shows that in the majority of cases, there are 

very few drowsy patterns during the morning driving sessions. During the night driving 

sessions, when the drivers were highly sleep deprived, there are a large number of drowsy 

pattern s, indicating driver sleepiness. On the average, the ANN classified 9 percent of 

the total morning driving intervals as drowsy while for the night driving this increased to 

50 percent. Table A.3 shows these percentages for individual drivers. 

Table A.3: Percentage of drowsy intervals identified by ANN 

Driving Subject Number 

Session 1 2 3 4 5 6 7 8 9 10 11 

Morning 7.62% 6.5% 13.8% 12.7% 10.5% 9.76% 13.0% 3.2% 13.8% 13.8% 4.0% 

Night 36.7% 48.2% N/A 37.5% 50.2% 50.8% 62.4% 61.4% 56.8% 54.0% 43.4% 

152
 
Subject 01 two day driving 

23344556677889

100111122133144155166177188199210221232243254265276287298309320331342 

15 sec time intervals 

St + Eye 

0 

1 

1

12 

Drowsy Wake Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

Fraction of time eye is closed 

Steering 

Morning 

0 

1 

Night 

Subject 02 two day driving 

2 

0 

1 

1

14

7

4053667992

105118131144157170183196209222235248261274287300313326339352365378391404 

15 sec time intervals 

St + Eye 

Drowsy Wake Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

Fraction of time eye is closed 

Steering 

Morning Night 

0 

1 

Subject 03 two day driving 

0 

1 

159

13172125293337414549535761656973778185899397

101105109113117121 

15 sec time intervals 

St + Eye 

Drowsy Wake Crash 

Morning 

0 

0.2 

0.4 

Eye 

0 

1 

Steering 

Fraction of time eye is closed 

Figure A.2: ANN output for subjects 1 to 3 

153
 
 

Subject 05 two day driving 

0 

1 

1

1325374961738597

109121133145157169181193205217229241253265277289301313325337349361 

15 sec time intervals 

St + Eye 

Drowsy Wa ek Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

F tion of time eye is closed rac 

0 

1 

Steering 

Morning Night 

Subject 06 two day driving 

0 

1 

1

1223344556677889

100111122133144155166177188199210221232243254265276287298309320331342 

15 sec time intervals 

St + Eye 

Drowsy Wake Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

Fraction of time eye is closed 

0 

1 

Steering 

Morning Night 

Subject 04 tw 

0 

1 

18

222936435057647178859299

106113120127134141148155162169176183190197204211218225 

15 sec time intervals 

St + Eye 

o day driving 

15 

Drowsy Wake Crash 

Morning 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

F tion of time eye is closed rac 

0 

1 

Steering 

Night 

Figure A.3: ANN output for subjects 4 to 6 

154
 
Subject 08 two day driving 

0 

1 

1

112131415161718191

101111121131141151161171181191201211221231241251261271281291301 

15 sec time intervals 

St + Eye 

Drowsy Wake Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

Fraction of time eye is closed 

0 

1 

Steering 

Morning Night 

Subject 09 two day driving 

0 

1 

1

1223344556

265276287298309320331342 

15 sec time intervals 

St + Eye 

677889

100111122133144155166177188199210221232243254 

Drowsy Wake Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

Fraction of time eye is closed 

0 

1 

Steering 

Morning Night 

Subject 07 two day driving 

7

131925313743495561677379859197

103109115121127133139145151157163169175 

15 sec time intervals 

St + Eye 

0 

1 

1 

Drowsy Wake Crash 

0 

0.2 

0.4 

0.6 

0.8 

Eye 

Fraction of time eye is closed 

Morning 

0 

1 

Steering 

Night 

Figure A.4: ANN output for subjects 7 to 9 

155
 
 

Subject 10 two day driving 

0 

1 

1

112131415161718191

101111121131141151161171181191201211221231241251261271281291 

St + Eye 

Drowsy Wake Crash 15 sec time intervals 

0 

0.2 

0.4 

0.6 

Eye 

Fraction of time eye is closed 

0 

1 

Morning 

Steering 

Night 

Subject 11 two day driving 

0 

1 

11

301 

St + Eye 

112131415161718191

10111112113114115116117118119120121122123124125126127128129 

Drowsy Wake Crash 15 sec time intervals 

0 

Eye 0.2 

0.4 

Fraction of time eye is closed 

0 

1 

Steering 

Morning Night 

Subject 12 one day driving 

0 

1 

17

131925313743495561677379859197

103109115121127133139145151157163169175181 

15 sec time intervals 

St + Eye 

Drowsy Wake Crash 

Night 

0 

0.2 

0.4 

0.6 

8 

Eye 

1 

0 

Steering 

0. 

Fraction of time eye is closed 

Figure A.5: ANN output for subjects 10 to 12 

156
 
 

COMPARISON WITH PERCLOS MODEL 

PERCLOS is the percentage of time the eye is more then 80% closed. Although 

PERCLOS is one of the widely accepted measures in the scientif ic comm unity for 

drowsines dete tion, it s c has the follow ing limitations: 

•	 PERCLOS predictions are good only when using large time intervals 

•	 PERCLOS does not take into account variability in eye blinking behavior 

between individuals 

•	 PERCLO S measures usually re quire obtrusive in -vehicle equipment 

•	 An eye tracker system can present reliability and measuring robustness problems 

(see section on Eye Closure Data in Chapter 4) 

Although a direct interval-by-interval comparison is not possible due to these limitations, 

a comparison with PERCLOS data calculated over 6-minute intervals shows good 

correlation. In more than 80% of the PERCLOS six minute drowsy intervals, the ANN 

outp t sho s on u w e oor m re 15 second drowsy 	 i ntervals. Figure A.6 shows the PERCLOS 

and ANN output for three drivers. 

COMBINED ARTIFICIAL NEURAL NETWORK MODEL 

This ANN model has was trained on data from both eyes and steering. The design and 

architecture of the ANN was similar to the one explained in Chapter 4 with the following 

architecture: 

•	 The input layer has 12 neurons corresponding to the input vector X(n) 

•	 The hidden layer has 27 neurons 

157
 
 

 

 

 

 

PERCL 5 OS 

0.1 

0.3 

0.4 

14 15 

6 minute Time Intervals 

5 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 

Wake 

Drowsy 

0 

1 25 49 73 97 121 145 169 193 217 241 265 289 

15 sec Time Intervals 

1 

Morning Night 

313 337 

Steering 

Drowsy Wake crash 

PERCLOS 

0 

0.15 

0.3 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 

6 minute Time Intervals 

Wake 

Drowsy 

0 

1 

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 

15 sec Time Intervals 

Steering 

Drowsy Wake crash 

Morning Night 

PERCLOS 0.45 

0 

0.15 

0.3 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 

6 minute Time Intervals 

Wake 

Drowsy 

0 

1 

1 25 49 73 265 289 313 337 

15 sec Time Intervals 

Steering 

Morning 

97 121 145 169 193 217 241 

Dro sy w Wake crash 

Night 

158 

Figure A.6: Comparison with PERCLOS (6-min time intervals) 
 ANN TRAINING (STEERING + EYE) 

Data from 10 subjects, which make a total of 1,600 driving intervals (as previously 

explained), was randomized and then divided into three separate files: 

• Training file, which includes a set of 900 input-output exem plar vectors 

• Cross validation file, which includes a set of 200 input-output exem plar vectors 

• Testing file, which includes a set of 500 input-output exemplar v ectors 

These files contain input vectors X(n) and output vectors D(n), each representing 15 

seconds of driving. Table A.4 and Figure A.7 show a portion of the input file. 

Table A.4: ANN performance during training (steering+eye) 

Best Networks Tra ginin Cross Validation 

Epoch # 1,000 764 

Minimum MSE 0.096 0.0773 

Final MSE 0.096 0.078 

MSE versus Epoch 

0.6 

0.7 

0.8 

0.1 

0.3 

0.4 

0.5 

MSE

Training MSE 

Cross Validation MSE 

0.2 

0 

1 100 199 298 397 496 595 694 793 892 991 

Epoch 

Figure A.7: ANN training performance 

159
 
ANN TESTING (STEERING + EYE) 

The network classified the test data into wake and drowsy driving intervals with an 

accuracy of more than 92%. This classification is shown in Table A.5. 

Table A.5: Test results (steering+eye) 

OUT PUT 

DESIRED 

WAKE DROWSY 

WAKE 179 16 

DROWSY 14 191 

% CORRECT 93 92.3 

The network correctly identified 191 out of a total of 207 drowsy intervals, an accuracy 

of 92.4%. There were 14 “false alarms”, intervals that were in wake class but were 

misclassified as drowsy. Also, 16 intervals that were in the drowsy class were 

misclassified by the network as wake. 

CRASH PREDICTION (STEERING + EYE) 

All drivers who completed the night driving session, with exception of subject 09, fell 

asleep while driving and crashed the vehicle one or more times. The network identified 

sleep patterns before the crash with an accuracy of 100 percent. Figures A.2 – A.5 show 

that in all these cases, the ANN is continuously identifying drowsy intervals before a 

crash. 

TESTING ALL SUBJECTS (STEERING + EYE) 

Data from both morning and night experimental driving sessions of all the subjects was 

presented to the ANN, on e subject at a time. The ANN output, fo r both morning and 

night experimental driving sessions, of all the subjects is shown in Figures A.2 – A.5. 

These figures show that during the morning driving sessions the ANN identified very few 

drowsy intervals and, therefore, few false alarms. There are a total of 75 false alarms 

(i.e., 6% of the total morning driving intervals). During the night driving sessions, when 

the drivers were sleep deprived, the ANN identified the majority of the intervals as 

drowsy. As shown in Table A.6, The ANN identified 60 percent of the total night driving 

intervals as drowsy. 

160
 
Table A.6: Percentage of drowsy intervals identified by ANN 

Session 

Subject Number 

1 2 3 4 5 6 7 8 9 10 11 

Morning 9.5% 9.8% 12.2% 12.7% 8.1% 5.7% 2.0% 1.6% 8.1% 6.5% 0.0% 

Night 60.4% 47.2% N/A 73.3% 63.0% 47.1% 97.6% 52.8% 85.0% 57.2% 41.2% 

Although the eye closure data was used in training the ANN, a comparison of the ANN 

output and PERCLOS show a high correlation. In Figure A.8, each bar represents the 

percentage of those driving intervals, which were classified as drowsy by the ANN and 

also for which the value of the corresponding eye closure interval is above the drowsy 

threshold. The threshold value used here is the value of PERCLOS that is 30% higher 

then the average value of the morning driving session. 

100 

0 

1 2 3 4 5 6 7 8 9 10 11 

25 

50 

75 

12 

Subject No. 

% correct 

Figure A.8: PERCLOS comparison 

161
 
 

APPENDIX B. DESIGN, FABRICATION, AND 

ADAPTATION OF HARDWARE ELEMENTS FOR CISR 

TRUCK SIMULATOR 

This appendix provides a detailed technical specification of the CISR Truck Simulator. 

Specific items that are discussed include the steering algorithm, the steering system 

mounting, the truck cabin preparation, the display screens, the gearbox mounting, the 

truck cabin pedals, the cabin adaptation and instrument interfacing, the sound systems, 

and the driving scenario development. 

STEERING ALGORITHM/MODEL 

The steering system is based on the INRETS haptic steering feedback algorithm. The 

Mohellebi and Espié steering algorithm is based on models used in robotic tele-operation. 

This model considers that the torque generated on the steering shaft is equivalent to the 

torque generated on the wheel pivot. Also, taking into account the induced rolling effect, 

the wheel torque is given by: 

Γ = aM γ − bMg θ wheel 

where g is the gravity, M is the vehicle mass, a and b are vehicle specific constants, θ is 

the roll angle, and γ is the lateral acceleration. The steering shaft torque is then given by: 

Γ − K wheel α α Γ = shaft G 

where α is the steering angle, Κα is the steering shaft linkage stiffness, and G is the gear 

reduction ratio (approximately 20). 

The adopted closed loop controller is shown in Figure B.1. αd is the desired steering 

angle, τd is the human applied steering torque, and τs is the motor measured torque. 

Vehicle 

model 

+ 

Torque

Sensor 

Torque

model Cf(s) 

τ s 

K 

1 +

s 

1 

τ s

v 

Motor model 

+ + + 

d τ + 

α d 

Figure B.1: Closed-loop controller 

162
 
STEERING SYSTEM MOUNTING 

In order to increase the realism inside the truck cabin, only the original components of the 

truck steering system are utilized. The original steering wheel, steering shaft, universal 

joints, and mountings are used. Additional steering hardware is installed in the front 

under the hood area, not visible to the driver. A steel frame was designed and fabricated 

to hold the steering hardware. This support plate was carefully designed to hold the 

steering shaft at the proper angle aligned with the steering. This assembly is designed to 

provide flexibility for adjusting the angle and positioning of the hardware. When the 

whole system was finely adjusted and tested, it was welded together and then to the truck 

cabin body. Figure B.2 shows details of the steering assembly, the support steel frame 

attached to the truck cabin, and the steering shaft connection elements. 

Figure B.2: Steering assembly, steering system mounting 

163
 
Figure B.2: (continued) Steering assembly, steering system mounting frames and 

detail of steering shaft connection 

164
 
Figure B.2 (continued): Steering assembly, steering system mounting frames and 

detail of steering shaft connection 

TRUCK CABIN PREPARATION 

The truck cabin and its hood were acquired from Pennsylvanian-based William Cohen & 

Sons Inc. The cabin and hood was shipped to the GWU campus in Ashburn, VA, see 

Figure B.3. 

Figure B.3: Truck cabin being delivered 

The truck cabin was temporary placed on four adjustable jacks to be able to fine-tune its 

final location and height. This low-cost solution provided both a sturdy foundation for 

the truck cabin and some flexibility in its height adjustment. CISR then designed and 

fabricated a rectangular steel frame base for the truck cabin after all the fixed position 

165
 
was finalized. Two longitudinal channel steel sections were connected across by two 

right angle steel sections, which were supported by four steel legs. The whole assembly 

was bolted to the truck cabin. This arrangement provided durable support for the cabin 

and other equipment, including the gearbox and steering torque system. Figure B.4 

shows the cabin mounted on temporary adjustable jacks and the drawing of the finalized 

cabin base/foundation. 

Figure B.4: Truck cabin on temporary jacks and final support base 

166
 
Most of the simulator control elements, such as the steering feedback system, brake and 

clutch position sensors, data acquisition system, etc., are located in the front area under 

the hood. To make these elements more easily accessible, CISR designed and fabr icated 

a movable platform to mount the hood. 

The cabin was then prepared for instrumentation and integration. The inside of the cabin 

was entirely disassembled, all the unwanted material was striped off, and all com ponents 

were cleaned, labeled, and reintegrated to the cabin, as shown in Figure B.5. 

Figure B.5: Dashboard wiring, auxiliary controls removed, pedals disassembled, 

and steering components removed 

DISPLAY SCREENS 

For the forward projection system, CISR designed three flat screens. The front projected 

images can be up to 9 ft wide by 6 ft high. To allow some flexibility in the image height 

adjustment, each front screen is 9 ft wide by 6.5 ft high. When placed together in the 

available 19.5 ft wide room, they provide a field-of-view of 135 degrees. 

167
 
These screens were custom-designed by CISR and were fabricated in-house. Each of 

these three screens consists of the following parts: 

•	 Supporting Frame: The dimensions of the frame are 13 ft high and 6 ft wide 

and .is fabricated using steel pipes and the frame is secured to the wall by 

adjustable steel fasteners. The frame is mounted on cast iron flanges secured to 

the ground. Specially designed aluminum blocks are attached to accommodate 

the screen, which is placed at a height of 3 ft from the ground. 

•	 Screen Frame: Figure B.6 shows the detail design of the screen frame. The 

rectangular screen frame is fabricated with rectangular steel tube sections. These 

sections were cut to size, drilled, and fastened to the aluminum blocks in the 

supporting steel frame. Tubular sections provide a very rigid and lig htweight 

design. 

•	 Screen Fabric: A special screen fabric was wrapped around the screen frame to 

provide a flat surface for display. This fabric is custom-ordered to fit the screen 

dimensions and has a white mat finish and a non-reflective surface. Small gage 

wire rope is used to wrap the fabric around the frame. The whole design is such 

that only the front white surface is visible to the viewer; everything else is be hind 

the screen. 

Figure B.6: Front projection screen 

Figure B.7 shows the completed frame structures in final location. 

168
 
Figure B.7: Figure B.7: Front screens frame structure and support Front screens frame structure and support 

The design used for rearview m The design used for rearview mirrors is sirrors is similar to those for the forward projection ilar to those for the forward projection 

screens. screens. The overall dim The overall dimension of tnsion of these screens hese screens is 4 ft wide by 4 ft high. is 4 ft wide by 4 ft high. The framThe frames are 

made out of alum ade out of aluminum, directly hung from , directly hung from the top at two points, and do not require an e top at two points, and do not require an 

additional sadditional supporting fram upporting frame. Steel beamSteel beams initias initially used for the truck roo lly used for the truck roof equipments 

(i.e., horn, lights, etc.), were m (i.e., horn, lights, etc.), were modified to provide adapted suppor provide adapted support to hang the rear t to hang the rear 

screens’ frames. The special fabric f The special fabric for these screens allows for both front and back ese screens allows for both front and back

projection.projection. Due to light cons Due to light constructiontruction and easy mounting th and easy mounting they are ideal for use in such an eal for use in such an 

environmenvironment. Figure B.8 shows the com Figure B.8 shows the completed screen.leted screen. 

Figure B.8: Rear screens 

GEARBOX MOUNTING 

To mount the gearbox system in the right position, CISR designed and fabricated a steel 

frame and housing. Many considerations were taken into account to make sure that any 

modification did not change the original character of the truck cabin interior. Figure B.9 

169
 
FigureB.9: Final assembly and mounting frame for the gearbox system 

shows the detailed design of the mounting frame. A rectangular frame was constructed 

using four right angle steel sections. Two longitudinal sections were welded to the cabin 

support frame. The other two sections were bolted to the lower support plate of the 

gearbox. These two sections were then bolted to the welded sections. Longitudinal 

rectangular slots were machined into all the four sections instead of drilling round holes. 

170
 
This provides a mechanism for adjusting the position of the gearbox inside the cabin. 

The whole frame was lowered so that the top plate is at the same level with the cabin 

floor. 

The gear change lever (stick) was modified to adapt to the truck cabin configuration. It 

was redesigned by changing the angle and length, for easy reach by the driver. The final 

position of the shift lever was determined through subjective evaluation by various CISR 

members. Because of the adjustable design of the mounting frame, it was possible to 

fine-tune the final position of the stick. The original cabin floor had to be cut to fit the 

new gearbox assembly. 

In the real truck, clutch and brake pedals were connected to hydraulic systems for 

controlling gear shifting and braking. In the driving simulator, these hydraulic elements 

were removed and replaced with a different mechanism that reproduces the feel of the 

pedals. Simple passive systems, based on spring load and small hydraulic resistance, 

were designed, and these systems produced good results once calibrated. The design, 

modification, and operation of the new simulator pedal systems are discussed in the 

following sections. 

Brake Pedal 

The original brake pedal of the truck was modified. Figure B.10 details the construction 

of the new brake pedal pr essure feel system. The braking pressure or feeling in the pedal 

is created through a combination of hydraulic and spring mechan ism. Pressing on the 

foot pedal (1) causes the roller (3) to press against the rubber housing (4). The 

compression spring (5) and piston (6) creates the resistance force against the pedal 

movement. The initial pressure feel is generated by the spring (3), further movement of 

the pedal causes the rubber housing to press against the hydraulic cylinder. This 

combination of spring and hydraulic pressure creates a very realistic feeling in the pedal. 

The hydraulic cylinder from the truck original braking system is used for a more realistic 

feeling. 

The pedal motion is transmitted to the controller card through an optical encoder. Figure 

B.11 shows a detailed diagram of the encoder mounting mechanism. The pivot shaft (2) 

and pedal hub (3) are joined together by retaining pins (6). The pivot shaft (2) is 

supported at two points by steel bearings housed in the mounting bracket. The encoder 

(7) is screwed to support plate (5), which is welded to the mounting bracket. The encoder 

shaft is connected to the pivot shaft through a flexible coupling (4). Pushing on the foot 

pedal (1) causes the pivot shaft to rotate, which in turn causes the encoder to rotate, 

allowing for the detection of movement in the pedal. 

171
 
Figure B.10: Modified brake pedal 

Figure B.11: Brake pedal encoder support assembly 

172
 
Clutch Pedal 

The clutch pedal and its mountings inside the cabin are all original. The modifications 

and encoder housing are located outside the cabin in the front firewall area. Figure B.12 

depicts the construction and operation of the new clutch pedal pressure feel system. The 

feeling of clutch action in the pedal is created through a spring lever mechanism. 

Pressing on the foot pedal (1), which swivels on pivot pin (2), causes a connecting rod (6) 

to raise the crank lever (4). This motion turns the two crank levers (4) in 

counterclockwise direction and pulls against the extension spring (8). This spring 

pressure creates the resistance or load in the pedal. 

Figure B.13 shows a detailed diagram of the encoder mounting mechanism. Pressing the 

clutch pedal causes hollow tube (8) to rotate about pivot shaft (2). A hollow cylindrical 

connector (6), which is welded to a hollow tube (8), transmits this rotary motion to the 

encoder (7) through a flexible connector (1). A support bracket (3) holds the encoder at 

one end of the tube (8). The other end of the hollow tube is bolted to the truck cabin (5). 

Pushing on the foot pedal causes the pivot shaft to rotate, which in turn, causes the 

encoder to rotate, allowing for the detection of movement in the pedal. 

Figure B.12: Modified clutch pedal 

173
 
Figure B.13: Clutch pedal encoder mounting assembly 

Throttle Pedal 

The throttle pedal resistance mechanism is generated with the original truc k throttle pedal 

system. Figure B.14 shows the working of the throttle pedal pres sure f eel system and the 

Figure B.14: Throttle pedal and encoder assembly 

174
 
throttle encoder mountings. A torsion spring and gear mechanism produces resistance in 

the pedal. Pressing the throttle pedal will turn a spring-loaded plastic outer gear (9) that 

meshes with inner gear/sector (10). This gear and spring mechanism is enclosed by an 

aluminum housing (4). Circular motionof the inner gear is transmitted to the encoder (7) 

through a small aluminum coupling (3) and a flexible coupling (1). A support assembly 

(5) supports the encoder and both couplings. The whole assembly is attached to the cabin 

through a steel bracket (6). 

CABIN ADAPTATION AND INSTRUMENT INTERFACES 

Gauges (speedometer and tachome ter gauges), instruments (turning indicators, vehicle 

lights, start key), controls (pedal o ptical encod ers), a nd switches (cabin dome-light, radio, 

etc.) of the truck cabin were connected to the simulation software system for real-time 

operation. 

To prevent potential voltage losses in the optical encoders and connecting wires, a 

voltage adaptation layer was designed. This unit also enables secure wiring between 

vehicle cabin digital signals and inputs of the CEGI card, as shown Figure B.15. 

Pull-up resistors for 

optical encoders 

Clutch 

Brake 

Throttle 

Cabin 

instruments 

Figure B.15: Connection card for pedals optical encoders and cabin digital inputs 

Because the CEGI card is operating under 5 volts, an additional layer of adaptation was 

required to manage the 12 volt cabin instrument, including the dashboard indicators and 

warnings, cabin dome light, radio, etc. A relay card was designed to switch from 5 volts 

to 12 volts as illustrated in Figure B.16. 

175
 
 

 

Relays 

R1 R2 R3 R4 R5 

GND 

+12 

Signal 

from 

ignition 

key 

Signal 

from left 

blinker 

Signal 

from 

high 

beam 

Signal 

from right 

blinker 

To water and 

fuel gauges 

To dashboard 

and instrument 

cluster 

backlighting 

To 

dashboard 

right blinker 

indicator 

To 

dashboard 

left blinker 

indicator 

To 

dashboard 

high beam 

indicator 

Voltage divider 

resistors 

Figure B.16: Circuit diagram of the cabin relay card and finalized electronic unit 

SOUND SYSTEM 

Sound plays an important role in the realism of driving simulation. Wind and engine 

noise contribute to fatigue in drivers who have logged many hours. Sirens and horns can 

divert driver’s attention away from the task at hand. Traffic noise can also affect a 

driver’s state of being and decision-making. Squealing tires are indicators that the car is 

being pushed towards its handling limits. The CISR simulator sound system is capa ble of 

generating both the t raffic sounds (i.e., other vehicle sounds, engine/acceleration sound, 

braking noise, etc.) and the vibrations transm itted to the vehicle cabin during driving. 

The human brain is able to determine the source of a sound it perceives by analyzing the 

input it receives from each ear. To create an immersive experience, sounds in the vir tual 

environment must replicate this physical phenomenon. That is, they must be given a 

sense of locality. To do this, the simulation sound generator must manipulate the speak er 

outputs to trick the brain into perceiving that the sound is coming froma specific location 

in space. 

A multi-chann el sound system composed offive speakers and a subwoofer is strategically 

placed in the cabin to optimize the global so und rendering. The subwoofer is attached to 

a steel plate, which is bolted to the truck cabinto create vibrations in the cabin during 

simulations, see Figure B .17. 

176
 
 

Figure B.17: Subwoofer in place of the engine 

CREATION OF THE DRIVING SCENARIO 

The research team at CISR has developed a driving scenario representing a monotonous 

driving environment that is likely to induce drivers’ boredom and drowsiness. This 

scenario includes realistic sounds, visual effects, and other pre-defined events regarding 

road traffic and traffic controls. The driving scenario included both the geometric data 

for a section of I-70 and the highway signs and other 3 dimensional objects on this 

section of roadway. 

Interstate I-70 Geometric Data 

The road network selected for the driving scenario of the drowsiness experiments is 

based on a section of I-70 in Kansas. The research team acquired construction drawings 

from Kansas DOT and extrac ted the following alignment and m arking data (complete 

geometric data is provided in Table). The codes in this table include: 

BVC = Beginning of vertical curve 

PI = Point of intersection 

EVC = End of vertical section 

LVC = Length of vertical Curve 

BC = Beginning of curve 

EC = End of curve 

Δ = Deflection angle (degree, min, sec) 

Rad = Radius of horizontal curve (ft) 

177
 
 

Color = Yellow Color = White Color = White 

Left Shoulder 

Width = 4" Width = 4"	 Width = 4" 

Style = Continous Style = Continous	 Style = Dashed 

Length of Each Segment = 10 ft 

Distance between segments = 30 ft 

Tan = Length of tangent (ft) 

LN = Length of horizontal curve (ft) 

R = Right hand curve 

L = Left hand curve 

The roadway marking diagram is shown in Figure B.18. Other data for this roadway 

includes the following: 

Number of lanes = Two in each direction 

Lane width = 3.6 m (12 ft) 

Right Shoulder width = 3 m 

Left shoulder width may vary between 2- 3 m 

Right Shoulder 

Figure B.18: Roadway marking 

A three-dimensional road geometry was created based on this geometric data. Figure 

B.19 shows the original geometric data on the left, a view of the modeled roa dway terrain 

in the middle, and an overview of the model of the entire terrain on the right. 

Figure B.19: I-70 cross section drawings and corresponding simulation geometry 

created using SGI OpenGL Performer 

178
 
Highway Signs and Other Roadway Scenery 

To complete the driving environment a variety of 3 dimensional objects were designed 

and placed on the roadway geometry, including: 

•	 Roadside objects, such as villages, trees, bushes, shown in Figure B.20 

•	 Traffic signs, shown in Figure B.21 

•	 The driving scene background, shown in Figure B.22 

•	 Typical U.S. vehicles were created by INRETS based on pictures provided by 

CISR and integrated to the driving scenario, shown in Figures B.23 and B.24 

Figure B.20: 3D roadside elements included in the driving scenario 

Figure B.21: Traffic signs placed on the roadway network 

Figure B.22: Driving scene background 

179
 
 

Figure B.23: Picture and corresponding 3D model of a Dodge Durango 

Figure B.24: Pictures and corresponding 3D model of a Century tractor semi-trailer 

180
 
Table B.1: Geometric design data for selected porti on of I-70 

STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

Sec 1 

0 0.000 -0.05 

1070 0.203 0.89 1070 1220 1370 300 

3500 0.663 -2.13 3500 4350 5200 1700 

5407 1.024 54 7 0 57 0 7 61 8 2 14,2 ,46 5 286 .79 4 

5290 1.002 -0.04 5290 5640 5990 700 

11044 2.092 11044 11898 12703.7 33,11,36 2864.79 

12077 2.287 0.62 12077 12377 12677 600 14204 2.690 3. 5 0 14 04 2 14 55 6 15 04 1 9 0 0 

15875 3.007 -2.54 15875 16915 17955 2080 

17515 3.317 17515 18174 18829.24 5,15,25 14323.9 

18182 3.444 -0.45 18182 18532 18882 700 

181
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

19609 3.714 19 09 6 21 24 6 23 15 6 14,4 ,22 1 15 26 6 21990 4.165 -0.56 21990 22137 22290 300 23300 4.413 

Sec 2 

23600 4.470 23600 25244.25 26888.5 22,05 8526.86 

25300 4.792 -1.2 25300 25700 26100 800 26750 5.066 -0.3 26750 27050 27350 600 30700 5.814 0.3 30700 31000 31300 600 31600 5.985 4 31600 32000 32400 800 32129 6.085 32 29 1 345 3.4 7 370 7.8 1 45,3 ,56 1 577 .58 1 33850 6.411 -3 7.2 33 50 8 35 00 2 36 50 5 27 0 0 

38350 7. 326 -0 4.2 38 50 3 38 00 7 39 50 0 7 0 0 

41100 7.784 2.05 41100 41700 42300 1200 

182
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

42700 8.087 3. 8 9 42 00 7 43 00 0 43 00 3 6 0 0 

46450 8.797 0.25 46450 47200 47950 1500 

48600 9.205 3.92 48600 49000 49400 800 

49850 9.441 -0 5.2 49 50 8 50 50 8 51 50 8 20 0 0 

50399 9.545 50 99 3 516 7.4 4 528 5.8 9 4,0 ,41 9 343 .4

77 

52350 9.915 -1.66 52350 52750 53150 800 55550 10.521 -3 7.4 55 50 5 56 00 0 56 50 4 9 0 0 

60200 11.402 -0.83 60200 60700 61200 1000 

62300 11.799 0 62300 62600 62900 600 63377 12.003 3.84 63377 63839.5 64302 925 64400 12.197 

Sec 3 

65729 12.449 -1 65 29 7 66 54 4 67 79 1 14 0 5 

183
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

66512 12.597 6 2651 67278.74 68045.48 11, ,54 33 7597 

68004 12.880 0.24 68004 68304 68604 600 71204 13.486 2.76 71204 71504 71804 600 71663 13.573 71663 72730 73797 10,37,45 11503.9 73829 13.983 0.84 73829 74129 74429 600 75904 14.376 2.44 75904 76204 76504 600 79861 15.125 79 61 8 813 3.5 8 82 06 9 10,0 ,36 7 17 31 2 

80629 15.271 -3 6.5 80 29 6 81 04 5 82 79 3 17 0 5 83829 15. 77 8 -0 . 2 83 29 8 84 29 1 84 29 4 6 0 0 

85204 16.137 0.24 85204 85354 85504 300 

87204 16.516 3.04 87204 87504 87804 600 89229 16.899 0.56 89229 89604 89979 750 89914 17.029 89 14 9 91 85 0 92 56 2 7,4 ,45 9 17 45 1 

184
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

91814 17.389 2.92 91814 92114 92414 600 93014 17.616 0 93014 93564 94114 1100 94604 17.917 -2.16 94604 95004 95404 800 

98060 18.572 9 0806 995 6.5 8 10 1311 15, ,32 19 1 4141 

99079 18.765 1 99079 99379 99679 600 

100454 19.025 -0.68 100454 100804 101154 700 

101834 19.287 -3.96 101834 102434 103034 1200 

105171 19.919 

Sec 4 

105469 19.975 -1 8.9 10 6954 10 9457 106119 6 0 5 

106971 20.260 -3 4.8 10 7169 10 6173 107751 7 0 8 

108841 20.614 1. 18 108841 109266 1096918 0 5 

109991 20.832 -4 109991 111071 112151 2160 

185
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

113371 21.472 0.28 113371 113731 114091 720 

114321 21.652 0.12 114321 114485 114649 328 116421 22.049 0 116421 116583 116746 325 118199 22.386 1.88 118199 118494 118789 590 119511 22.635 0.29 119511 119901 120291 780 121891 23.085 2.07 121891 122151 122411 520 125221 23.716 -3 7.5 12 2152 12 7162 127321 2100 128271 24.294 -2.268 128271 128466 128661 390 130671 24.748 0.542 130671 130931 131191 520 132165 25.031 

Sec 5 

132834 25.158 132 4.3 83 132 4.3 99 133 4.2 15 1,7 .3 ,4 164 4.1 0 

133743 25.330 -2.969 133743 134393 135043 1300 

186
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

136000 25.758 -3.561 136000 136195 136390 390 137840 26.106 -1 .493 13 4078 13 3580 138230 3 0 9 

139305 26.384 0. 797 13 0593 13 9294 139680 3 5 7 

140990 26.703 1. 57 14 9009 14 1516 142240 1250 142730 27.032 0.356 142730 142925 143120 390 144435 27.355 -2.004 144435 144960 145485 1050 

146167 27.683 146167.6 1472 8.3 2 14 8982 1,2 ,54 4 2 2618 

146255 27.700 -1.158 146255 146450 146645 390 147255 27.889 -1 2.9 14 5572 14 5074 147645 3 0 9 

148115 28.052 0.32 148115 148632 149150 1035 

149980 28.405 -0.24 149980 150275 150570 590 153275 29.029 -0.712 153275 153570 153865 590 155145 29.384 -0.235 155145 155440 155735 590 

187
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

157475 29.825 0 157475 157750 158025 550 160452 30.389 

Sec 6 

160727 30.441 160727.6 1620 6.3 2 16 2533 25, ,08 47 5 .9770 

161977 30.677 2.608 161977 162417 162857 880 162892 30.851 0. 643 16 9228 16 1732 163542 6 0 5 163582 30.981 -1.52 163582 163857 164132 550 164602 31.175 3.88 164602 164902 165202 600 

167217 31.670 167217.1 1684 0.3 7 1697 3.5 2 25, ,56 14 5 .6687 

169566 32.115 -1.108 169566 169891 170216 650 171052 32.396 -1.203 171052 171217 171382 330 172352 32.642 -0 .503 17 5223 17 7726 173002 6 0 5 

174177 32.988 1.278 174177 174677 175177 1000 

188
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

175152 33.173 1.739 175152 175447 175742 590 176052 33.343 0.312 176052 176282 176512 460 177784 33.671 177784.7 178250.6 178716.4 1,3 ,16 3 343 3.9 3 

174682 33.084 4 174682 175142 175602 920 180052 34.101 - 74. 18 5200 18 6212 182472 2420 

180717 34.227 180717.7 1811 3.5 9 181669.2 0,5 ,14 7 5 .5733 

183352 34.726 -0.185 183352 183812 184272 920 185302 35.095 -1.08 185302 185597 185892 590 188402 35.682 2. 87 18 0284 18 9288 189382 9 0 8 

189402 35.872 0. 436 18 0294 19 5200 190702 1300 191152 36.203 2.775 191152 191802 192452 1300 

195517 37.030 195517.6 195906.1 196 94.5 2 0,4 ,39 3 611 6.6 8 

194702 36.875 -3.44 194702 195767 196832 2130 

189
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

199752 37.832 4.108 199752 200472 201192 1440 

201202 38.106 -4.487 201202 202677 204152 2950 204177 38.670 1.38 204177 204734 205292 1115 206002 39.016 -2.35 206002 206652 207302 1300 207623 39.323 

Sec 7 

209023 39.588 4. 93 1400 2250 3100 1700 

210973 39.957 -1.459 3350 4325 5300 1950 

211190 39.998 211190.2 211731.5 212272.9 2,4 ,26 2 229 6.6 1 

212984 40.338 212984.8 2136 0.6 6 214336.5 2,4 ,35 2 286 8.9 0 

213423 40.421 -0 .354 21 2334 21 3635 213648.9 2 5 2 

215673 40.847 0.754 215673 215836 215998.9 325 217623 41.217 3.26 217623 217948 218273.9 650 

190
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

218773 41.434 -1 9.6 21 7387 21 7399 221173.9 2400 

221223 41.898 -0 .612 22 2312 22 7313 221523.9 3 0 0 

223723 42.372 0.469 223723 223923 224123.9 400 

225223 42.656 -3 .046 22 2352 22 9860 226973.9 1750 

227873 43.158 1. 557 22 7378 22 3684 228998.9 1125 

229228 43.414 229228 43.414 231078 43.765 0.598 231078 231328 231578 500 235278 44.560 -0 .683 23 7852 23 0356 235928 6 0 5 

236878 44.863 -2.016 236878 237340 237803 925 237020 44.890 237020 2372 3.2 3 2374 6.5 4 0,2 ,34 5 573 2.6 3 

241528 45.744 2. 302 24 2815 24 6519 242403 8 5 7 246728 46.729 -1.535 246728 247603 248478 1750 

191
 
STATION VERTICAL CURVE HORIZONTAL CURVE 

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(DMS) Rad(ft) 

247380 46.852 247380.9 247661.4 247941.9 2,4 ,40 7 1 6149 

248834 47.128 248834.3 249280.5 249726.7 4,28,52 11417.3 

249678 47.288 -2.873 249678 250103 250528 850 251429 47.619 

Sec 8 

252879 47.894 1. 40 25 7928 25 7931 253479 6 0 0 

254729 48.244 0 254729 255029 255329 600 255929 48.471 0.68 255929 256229 256529 600 258157 48.893 258157 260462 262767 28,25,47 9291 

258779 49.011 -1.36 258779 259379 259979 1200 

260464 49.330 3.2 26 6404 26 9410 261724 1260 

264242 50.046 264242.6 265280.1 266317.6 20,45,09 5729 

264529 50.100 -1.04 264529 265529 266529 2000 

192
 
STATION VERTICAL CURVE HORI ZON TA L CU RVE

FT Mile GR(%) BVC PI EVC LVC (ft) BC PI EC Δ(D MS) ad( R ft) 

266879 50.545 2.16 266879 22672 9 267579 700 268102 50.777 268102 268510.5 62 8919 ,8,10 33 5729 268879 50.924 -1.96 268879 72696 9 270479 016 0 270734 51.275 270734 271306.9 127 879.7 ,22,54 50 2864 272265 51.565 -1.68 272265 62724 5 272665 400 

272703 51.648 2 3 7270 273176.5 2 0 7365 18,57,29 2864 

273429 51.786 1.36 273429 72737 9 7412 2 9 700 274579 52.004 -4.52 274579 72755 9 2 9 7657 020 0 276975 52.457 2 5 7697 277517.5 2 0 7806 03,37,04 17188 

277309 52.521 -1.02 277309 62776 9 7802 2 9 720 277758 52.606 

193
 
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