Validation of Improved Research Integration Tool 

(IMPRINT) Driving Model for Workload Analysis 

by Josephine Q. Wojciechowski 

ARL-TR-3145 April 2004 

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Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5425 

ARL-TR-3145 April 2004 

Validation of Improved Research Integration Tool 

(IMPRINT) Driving Model for Workload Analysis 

Josephine Q. Wojciechowski 

Human Research and Engineering Directorate, ARL 

Approved for public release; distribution unlimited. 
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14. ABSTRACT 

The Human Research and Engineering Directorate of the U.S. Army Research Laboratory developed a model of the tasks and 

workload associated with driving a ground vehicle. The human performance modeling tool, Improved Performance Research 

Integration Tool (IMPRINT), was used to simulate the driving tasks. Perception, cognition, and motor control were represented 

in the IMPRINT driving model. Human processing, attention, and response were simulated as concurrent discrete events. 

Subsequently, the driving model was incorporated into other IMPRINT models used to investigate crew size and function 

allocation in Future Combat Systems (FCS) conceptual ground vehicles. Driving is a primary crew function in FCS ground 

vehicles. The results of this study indicated that a dedicated driver was recommended in combat vehicles. In all configurations 

tested, the driver was consistently the crew member with the highest workload. 

As expected, results of simulation runs were consistent with research on driving and distraction. Structural and output 

validation of the model was completed through literature review. Driving by itself is a high mental workload function. The 

human processing capacity is fully engaged in tasks when one is driving, with the primary load being in perception and 

cognition. Literature shows that performance will start to degrade if additional tasks are attempted during driving, especially if 

the tasks are highly perceptual or cognitive. 

This model provides a reasonably simple way to represent the driving function and can be used for investigating any system 

where driving is important. For FCS, this will include direct driving and teleoperations. Several additional validation studies 

are planned. 

15. SUBJECT TERMS

 driving; human performance; modeling; task network; workload 

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 Josephine Q. Wojciechowski 

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iii 

Contents 

List of Figures iv

List of Tables iv

1. Introduction 1

2. Background 1

3. Model Description 3

3.1 Model Functions..............................................................................................................4 

3.2 Model Assumptions.........................................................................................................9

4. Driving Validation 10

4.1 Structural Validation .....................................................................................................11 

4.2 Output Validation..........................................................................................................13 

5. Discussion and Future Work 14

6. References 16

Distribution List 17
iv 

List of Figures 

Figure 1. Primary mission task network. ........................................................................................4 

Figure 2. Move function task network............................................................................................5 

Figure 3. See function task network. ..............................................................................................6 

Figure 4. Maintain situation awareness task network.....................................................................6 

Figure 5. General communication task network.............................................................................7 

Figure 6. Avoid hindrance task network.........................................................................................8 

Figure 7. Remediation necessary task network...............................................................................9 

List of Tables 

Table 1. Test conditions for FCS function allocation study. ..........................................................2 
1 

1. Introduction 

Driving is a routine task performed every day. As people gain experience in this task, many of 

the required actions become “automatic.” This means that an experienced driver may not require 

the same level of mental effort as a novice (Schlegel, 1993). In today’s society and technological 

revolution, driving is sometimes secondary to activities such as talking on the telephone, reading 

electronic mail, eating, and determining the vehicle location on in-vehicle navigation systems. 

Likewise, in the U.S. Army, the transformation of the Army is calling for Soldiers to do more 

tasks with better technology. In many instances, combat vehicle crew size is being reduced. The 

idea is that with increased technology, the soldier will have more time to do more tasks. Recent 

investigations into combat crew function allocation have shown that driving is a large contributor 

to mental workload (Mitchell, Samms, Henthorn, and Wojciechowski, 2003). Allocating 

additional functions to the driver increases the potential for performance errors. It may not be 

safe to perform any secondary task while driving, even for experienced drivers. 

In 2001, the U.S. Army Research Laboratory (ARL) developed a model to investigate off-road 

driving from one location to another. This model represented tasks that were necessary to drive. 

The model was exercised in an experiment with three driving modes (direct driving, 

teleoperation, and semi-autonomous), two levels of obstacles (high and low frequency), and two 

levels of vehicular reliability (high and low) (Wojciechowski, Kogler, and Lockett, unpublished). 

Subsequently, in 2002, that off-road driving model was used in another study that investigated 

the allocation of function in a combat vehicle for the Future Combat System (FCS). The results 

of the second study indicated that driving was the highest workload contributor (Mitchell et al., 

2003). Any additional mental workload may cause errors. Because the driving function was so 

critical to the FCS crew function allocation study, this effort to establish the validity of the 

driving model was undertaken. The purpose is to validate the driving model as a component for 

use in this and other vehicle system models. 

2. Background 

Modeling and simulation are often used to predict human performance in situations when it is 

not feasible to run an actual field study or laboratory investigation. ARL developed a simulation 

tool called Improved Performance Research Integration Tool (IMPRINT), which is a discrete 

event simulation that measures system performance as a function of human performance 

(Allender, Kelley, Archer, and Adkins, 1997). IMPRINT includes measures of task and mental 

workload. 
2 

In 2001, IMPRINT was used to predict human performance in different driving modes 

(Wojciechowski et al., unpublished). The investigation compared mission completion time, 

mission success rate, and operator mental workload of several different driving conditions: 

driving control mode, obstacle rate, and vehicle reliability rate. The control modes of interest are 

driving the vehicle yourself while you are in it (self driving), controlling every aspect of a 

vehicle from afar (teleoperation), and supervising a semi-autonomous vehicle from afar (semiautonomous). The objective of the investigation was to develop a model of varying levels of 

operator control of a ground vehicle movement task to assess the effect on human information 

processing and total system performance. 

In 2002, this driving model was used with other combat functions in an investigation of function 

allocation for the combat variant of FCS (Mitchell et al., 2003). This study combined driving 

with other combat functions, gunning and commanding, to determine the number of crew 

members required to successfully complete a combat mission. The three major functions of 

commanding, gunning, and driving were distributed in different combinations of two-person 

crew and one allocation of three-person crew. The four conditions tested are given in table 1. 

Table 1. Test conditions for FCS function allocation study. 

Condition Function allocation 

1 Commander-Driver and Gunner 

2 Gunner-Driver and Commander 

3 Commander-Gunner and Driver 

4 Commander, Gunner, and Driver 

The results of the study indicated that driving was a high mental workload task. In each of the 

conditions tested, the driver was the crew member with the highest workload. Even in the 

conditions where no additional tasks were assigned, the driver had the highest workload. 

Additionally, the workload levels that were reached by the driver indicated that the driver was at 

or near the mental workload threshold. 

A validation effort was initiated when the results of this investigation revealed the importance of 

driving. Validation can be accomplished in different ways. Army regulation 5-11 (AR 5-11) 

states, “Validation is the process of determining the extent to which the M&S (model and 

simulation) adequately represents the real world from the perspective of its intended use” (1997). 

Department of the Army Pamphlet 5-11 (DA Pam 5-11) states there are two components to 

validation (1999). The first component is structural validation and it focuses on review of the 

assumptions and architecture of the model. The second component of validation is output 

validation. This type of validation compares the output of the M&S to the perceived real world. 

The present report addresses both structural and output validation of the driving model. 

To complete the validation effort, a complete description of the model and modeling assumptions 

is provided, followed by validation of the model components through comparison to other 

driving models. The observation that “driving is a high workload task” is consistent with data 
3 

from the literature. This demonstrates output validity. The validation is followed by a short 

discussion and description of future work. 

3. Model Description 

The model representing the three driving methods was developed in IMPRINT version 6 

(Wojciechowski et al., unpublished). IMPRINT has a task and workload analysis capability to 

assess the impact of task load demands on human and system performance (Allender et al., 

1997). This latest version of IMPRINT allows the representation of human tasks as goals. 

Because the driving task is a highly cognitive task, there is a need to represent human behavioral 

components. An important human information processing feature of IMPRINT is the capability 

to model mental workload demands. The VACP (visual auditory cognitive psychomotor) 

workload theory implemented in IMPRINT is discussed in detail in a U.S. Army Research 

Institute technical report (McCracken and Aldrich, 1984). Workload theory is based on the idea 

that every task a human performs requires some attentional resource demands. Some tasks are 

highly automated and require very low resources while others require full concentrated attention. 

Usually a task is composed of several different types of demands, such as visual or cognitive. 

IMPRINT is structured to help assign values representing the amount of effort that must be 

expended in each resource in order to perform each task. IMPRINT uses a list of scale values and 

descriptors for each resource channel. These scales are taken directly from Bierbaum, Szabo, 

and Aldrich (1989). Each scale ranges from 0.0 to 7.0 and has benchmarked textual descriptors 

corresponding to increasing demanding tasks in that channel. The descriptors correspond to 

increasing levels of human information processing activity within a given channel. Functions, 

tasks, and goals for the model were developed by hierarchical task analysis methods (Kirwan and 

Ainsworth, 1992) and were then augmented by cognitive task analysis methods to capture the 

non-physical aspects of driving and controlling vehicles (Cooke, 1994). Discussions with ARL 

subject matter experts about teleoperation and semi-autonomous driving were critical to this 

process. Task duration times not covered by modeling assumptions were developed on the basis 

of data and algorithms in literature (Wierwille, 1993; Archer and Adkins, 1999). 

In IMPRINT’s goal orientation option, the primary goal consists of the basic mission that must 

be accomplished (Archer and Allender, 2001). Other goals may conflict or interact with the 

primary goal and are triggered at appropriate times as needed to represent the changes in the 

system. For this model, driving from point A to point B represents the primary mission. The 

competing goals that are represented are obstacles, appearance of a threat, redirection from 

headquarters, vehicle being stuck, and detection of a vehicle malfunction. In other words, the 

driver has been directed to leave his current position and drive to another specified location. It 

was assumed that planning has been completed and the driver knows where he is supposed to go. 
4 

While the driver is traversing the terrain to his new location, he may encounter obstacles and 

other difficulties. All the goals that appear will interrupt the primary mission. 

3.1 Model Functions 

The primary mission includes the functions necessary to drive from point A to point B. It 

includes four functions, “move,” “see,” “maintain situation awareness (SA),” and “communication”. These functions all run concurrently in the model. Each of the functions is described 

separately (see figure 1). 

Figure 1. Primary mission task network. 

The “move” function (figure 2) includes tasks that represent steering (and non-steering) and 

controlling the speed of the vehicle (acceleration, deceleration, and coasting). These tasks are set 

to occur in a cyclical fashion, meaning that once the vehicle has initially accelerated, a 

probabilistic decision is made whether the driver will accelerate, decelerate, or coast. Once this 

task is complete, the probabilistic decision is polled again with the choice to accelerate, 

decelerate, or maintain speed. Initial speed is set. The increase or decrease in the acceleration 

and deceleration tasks, respectively, can be set to the desired level. A minimum and maximum 

speed can also be set. The driver also cycles through the steer (adjust the steering mechanism) 

and do-not-steer (hold steady the steering mechanism) tasks. 
5 

Figure 2. Move function task network. 

Included in the “see” function are the tasks of scanning the sector, detecting landmarks, threats 

and obstacles, recognizing the path, calculating the distance to objective, and comparing to the 

guidance (see figure 3). First, the driver scans the sector. Then, the model will probabilistically 

determine if he sees an obstacle, threat, or landmark. If an obstacle (any physical object to be 

avoided) or threat is detected, the goal function “avoid hindrance” is triggered and the primary 

mission is interrupted. If a landmark is detected, the driver continues his tasks. The tasks of 

recognizing the path, calculating the distance to the objective, and comparing to guidance 

received are performed simultaneously. These three tasks represent higher cognitive processes 

while “scanning the sector” represents the perceptual process. That is why scanning the sector is 

executed before the three cognitive tasks. These tasks repeat to form a feedback loop similar to 

that described in Wickens’ human information processing model (Wickens and Hollands, 2000). 

Another function that repeats is the “maintain situation awareness” function (see figure 4). This 

function was added to the model as a direct result of the cognitive task analysis performed for 

this study. This function consists of cognitive processes that include assessing the orientation of 

the vehicle, assessing the motion of the vehicle, assessing the traction of the vehicle, and 

awareness of vehicle function. These tasks represent the flow of Wickens’ model that does not 

require a lot of attentional resources and memory capacity. Particularly in a direct driving 

condition, they are highly learned and are performed almost automatically, based on cues from 

the environment. In teleoperated vehicles, the operator is required to devote much more 

attentional resources to assess these conditions. At the conclusion of the assessments, the model 

probabilistically determines if the vehicle has stopped moving or is malfunctioning. If either of 
6 

these conditions is true, the “remediation necessary” goal is triggered and the primary mission is 

interrupted. 

Figure 3. See function task network. 

Figure 4. Maintain situation awareness task network.
7 

The last function included in the primary mission is a “general communication” function (see 

figure 5). This function represents the operator communicating with other people outside his 

vehicle. The communications are modeled to be about 25 ±10 words; they are triggered 

randomly. At the end of each communication, the model will probabilistically determine if the 

communication leads to a redirection of the vehicle. If this condition is true, the “avoid 

hindrance” goal is triggered and the primary mission is interrupted. The tasks for redirection are 

similar to those for avoiding an obstacle. 

Figure 5. General communication task network. 

There are two goal functions in this model. (As mentioned previously, they are “remediation 

necessary” and “avoid hindrance.”) Remediation necessary is the higher priority of the two 

goals. However, because both goals are triggered in the primary mission and in both cases the 

primary mission is interrupted, these two goals cannot occur simultaneously. 

The “avoid hindrance” goal represents tasks associated with avoiding obstacles, threats, and 

tasks associated with changing path based on a redirection (see figure 6). If a threat is detected, 

the immediate tasks triggered are accelerate, “hard” steer, and plan escape route. Then the 

operator will check to see if the threat is still present. There is a 25% chance that the threat will 

not be clear. If so, the operator recycles through the accelerate, hard steer, and plan escape route 

tasks. If the threat is clear, the operator will execute the task, scan sector, and drive. This is the 

first task executed if an obstacle or a redirection triggers the goal. The path for all three goal 

triggers is the same from this point on. Once the sector has been scanned, the operator will 

recognize the paths, determine distance to objective, and compare to guidance received. Then 

the operator chooses a new path and speed. At this time, the goal ends and the primary mission 

resumes. 
8 

Figure 6. Avoid hindrance task network. 

The “remediation necessary” goal represents tasks that will be executed when the vehicle is not 

moving or not operating correctly (see figure 7). If the operator determines that the vehicle is not 

operating correctly, he will stop (brake) the vehicle. He will then assess the systems that he can 

without getting outside the vehicle. He will decide if the problem can be fixed. There is a 5% 

chance that the mission will be aborted at this point. There is a 35% chance that the vehicle is 

damaged and the mission can continue with damage. If this condition is true, the vehicle will be 

limited to 6 miles per hour maximum speed. There is also a 60% chance that the operator can 

make some control adjustments to fix the vehicle. After the control adjustments, the operator 

will drive the vehicle and determine if the problem is fixed. There is a 65% chance that the 

problem is fixed and a 35% chance the operator will go back to the “brake” task and start over. 

If the problem is fixed, the operator will scan the sector and drive. If “remediation necessary” is 

triggered because the vehicle is not moving, the operator will assess for damage first. There is a 

30% chance that damage has occurred and a 70% chance that there is no damage. If the vehicle 

is damaged, the operator will assess the damage to determine if it is reparable. At this point, 

there is a 5% chance that the mission will be aborted. There is a 35% chance that the operator 

will continue with the damage (a reduced maximum speed would then be set), and a 60% chance 

that the operator can make adjustments to repair the damaged system. After repair or if there is 

no damage, the operator will assess the vehicle’s orientation. If the vehicle is not upright, the 

mission is aborted. The probability of this is 5%. The operator will then assess vehicle 

entanglement and traction. The model represents a 5% probability that the operator cannot 

untangle the vehicle and the mission will be aborted. For the other 95%, the operator then makes 

adjustments, drives the vehicle, and assesses the situation. If the vehicle moves, the operator 

continues with scanning the sector and driving. If the vehicle is still stuck, the operator returns 
9 

to the assess orientation task and starts over. There is a 65% chance that the vehicle is unstuck. 

As in the primary mission function “see” and the goal “avoid hindrance,” after scanning the 

sector, the operator will recognize the path, determine the distance to the objective, and compare 

to his guidance. He will then select a new path and speed, and the goal ends. At this point, the 

primary mission resumes. 

Figure 7. Remediation necessary task network. 

3.2 Model Assumptions 

When a task network model is developed, it is very difficult to represent reality. Many 

assumptions must be made so that the process modeled can examine the area of interest without 

every detail of the process having to be modeled. This is true of the models developed for this 

study. The assumptions may have to do with the way the process is represented or with specific 

aspects of the process. The easiest method for describing the assumptions is to address them as 

they apply to the level of autonomy that was used as one of the independent variables in the 

original investigation of driving control mode. 

An off-road path was chosen for the scenario. It is assumed that the operator has already planned 

where he is supposed to go and that he is just starting. The vehicle, whether it was an actual 

vehicle driven by the operator or a robot, needs to traverse from the current position to some 

known position, 0.5 mile away. This distance can be adjusted. There may be threats and 

obstacles in the path. The operator may receive a communication that directs him to change 

course or the vehicle may become stuck or malfunction. 
10 

When the vehicle is driven directly, it is assumed that the operator stays in the vehicle. All 

operations, even repairing malfunctions or damage, can only be performed in the vehicle. This 

assumption was made so that the tasks for all three levels of autonomy are consistent. 

Several assumptions were made about the control mechanisms for the teleoperated and semiautonomous driving conditions. The workstation design that was modeled includes a video 

display and a joystick type controller. The display is a 120-degree field of view display with an 

operator-controlled switch that would shift the camera from left to right or vice versa. The 

joystick is assumed to be similar to those on many video games. The operator controls the 

direction and speed of the vehicle by moving the joystick forward to the left or right. He 

decelerates by pulling the joystick back. It is known that in both teleoperated and semiautonomous modes, the vehicle speed is less than in direct driving (Scribner and Gombash, 

1998). To represent this in the model, the probability that the operator will coast, decelerate, or 

accelerate can be adjusted appropriately in the “move” function. 

The level of obstacles, threats, malfunctions, redirections, and mishaps is represented by 

probabilities in the model. In the “see” function, using mean time calculations, it takes about 6 

seconds to complete all the tasks in the function. Using this time, one can then calculate the 

probability that an obstacle or threat would be encountered on each cycle through this function. 

The “communicate” function takes about 45 seconds. Again, this time can be used to set 

probabilities that the driver will be redirected. The “maintain situation awareness” function 

averages about 3 seconds. This time is used to adjust the probabilities that malfunctions would 

occur or that the vehicle would become stuck. 

The percentages that were chosen in most cases represented the scenario chosen for the model. 

They were calculated so that the model would produce the conditions for the tests undergoing 

consideration. These values can be adjusted to represent different environments or test 

conditions. 

4. Driving Validation 

Validation of the driving model with a field study was considered initially. The output of the 

model indicated that driving was a high workload contributor and other distractions (primarily 

visual and cognitive) would result in the potential for performance errors. Output validation of 

this model by direct comparison of model output to field data is costly. This is primarily because 

of the difficulty in measuring mental workload and the danger in exposing subjects to levels of 

mental workload that would lead to possibly dangerous performance errors. Mental workload is 

the primary measure in this model. Mental workload can be measured by many different means, 

primary task measures, secondary task measures, subjective measures of workload, and such 
11 

(Sanders and McCormick, 1993). The difficulty would be in correlating those measures of 

mental workload with the measures in IMPRINT. That idea was abandoned because the 

literature shows that mental processing limits are approached when one is driving a vehicle. 

Many studies have been completed which show that additional distracters from driving would 

result in performance errors. The errors can range from lane maintenance to vehicle accidents. 

This led to the establishment of validity by two methods. The first method of the validation is 

structural validation of the model constructs through comparison with other driving models. The 

second method, output validation, shows results from other studies that indicate driving is a high 

workload task. 

4.1 Structural Validation 

The first step in validating this model was to identify individuals or organizations that hold an 

expertise in driving and human behavior. Dr. John Lee of the University of Iowa has done 

extensive research in driving behaviors. The Virginia Tech Transportation Research Facility has 

also been involved in many years of human driving research. From these two sources and others 

identified through literature search, we were able to identify other research primarily in the area 

of modeling human driving behavior. 

The initial validation of this driving model is face validity, which validates what is in the model. 

Face validity was accomplished by a comparison of model constructs with other widely accepted 

driving models. Levison described a “Driver Performance Model” that was developed in 1993 

and has since been used as a basis for other driving models. The parts represented in Levison’s 

model include perception, cognition, control actions, and decision making. This model is 

actually two models combined, a driver-vehicle model and a procedural model. The drivervehicle model is a continuous feedback model between the driver’s actions and the vehicle 

reactions. The procedural model looks at the driving tasks and determines task selection as it 

simulates the in-vehicle auxiliary tasks. The procedural model represents the regulation of 

attention. These components are all represented in our driving model. 

Biral and Da Lio (2001) suggest that good driver models are required to predict vehicle 

performance. Their investigation revealed three main types of driver models. First, some 

models are based on conventional continuous control such as proportional integral derivative 

(PID) and generalized predictive control (GPC). The second type of driver models that exist are 

fuzzy logic or neural network-based controllers. Fuzzy logic controllers are popular for 

representing human behavior and neural nets for their capability to learn. Biral and Da Lio 

called the final class of driver model, hybrid and hierarchical models. These employ the other 

two previously described types. Of the driver models they identified, they determined that for 

models to represent realistic driving behaviors, they must functionally consider the following 

components: perception, cognition, decision, and motor process of the human. 
12 

Salvucci, Boer, and Lui (2001) use a cognitive architecture to model driver behavior. They 

characterized their model in terms of three primary components: control, monitoring, and 

decision making. The control component accounts for perception of control variables and motor 

control. The monitoring component accounts for monitoring the environment. The decision 

component is the cognitive process of determining if a lane change is necessary or safe. 

Brown, Lee, and McGehee (2000) described a driver model of a rear-end collision warning. The 

results are a time history of the driver’s response in avoiding a rear-end collision. It contains 

three major components. The first is a representation of the attention to the roadway based on 

the uncertainty of the driver. The second component describes the decision process for braking 

or travel. The third component describes the driver’s response. Again, these are the perceptual, 

cognitive (including decision making), and motor processes. 

Additionally, there has been some discussion about the adequacy of representing a continuous 

process (driving) with a discrete event simulation (ARL Technical Advisory Board, 2002). It 

can be argued that a continuous process can be represented in a discrete event simulation. 

Perhaps it is a series of discrete tasks that happen continuously with continuous input. The most 

continuous portion of driving would be the visual and cognitive processes. Even these can be 

described as discrete tasks. First, you glance ahead, then to the front left, then the front right, 

then the rear view mirror. At each glance, there would be perception and a decision about 

hazards, etc. If these tasks happen continuously, i.e., without a time interruption in the 

simulation clock, would that not represent a “continuous” process? 

The point here is that all the human information processes represented in each of these other 

models are also represented in our discrete event simulation model. No human information 

processing pieces have been excluded. The representations may be different, but that is expected 

because the purpose for each of the driver models is different. Most driver models are built in a 

closed loop system with the vehicle so that the actions taken by the driver model will impact the 

vehicle performance, and that will, in turn, impact the next action of the driver. Our model was 

built to determine the attentional demands that are controlled in Levison’s procedural model. 

The feedback loops with the vehicle are not represented in this model. The model is a stochastic 

model used to look at the different combinations of driving tasks that may happen concurrently. 

This provides the ability to identify how the driver’s mental demand varies and to identify areas 

for potential performance degradation. 

This model shows that the potential for performance errors during driving is great. There are 

many times in a model run when the driver’s mental workload is near or above what might be 

considered a mental workload threshold, based on work by Reid and Colle (1988). This would 

indicate that any distraction from driving would increase the probability of performance errors. 

The next step in validating the model was to determine if the last statement is true through 

literature review. 
13 

4.2 Output Validation 

The opportunity for distraction during driving is continually increased in today’s society. The 

technology that is currently in use or being developed such as cell phones, in-vehicle navigation 

systems, car stereos with compact disc and cassette players, and mobile business services can all 

cause distraction from the primary task, driving. In addition to the “high tech” distractions, 

today’s life styles offer other “low tech” distractions such as eating while moving, reading while 

commuting, applying make-up, or just concentrating on other aspects of our busy lives. Many of 

these distractions require similar mental loading to those tasks that would be required in military 

vehicles. 

Many different organizations are interested in studies of how these types of distraction impact 

the human’s ability to drive. Driver distraction and subsequent performance errors impact the 

safety of the vehicle. Therefore, insurance companies, automobile manufacturers, Government 

agencies, and other policy makers are all interested in this topic. As a result, many studies have 

been conducted to quantify and qualify the performance errors that may be caused by different 

driver distraction. Studies have been conducted in instrumented vehicles and in simulators. 

Almost all investigations show that any distraction from driving allows the potential for 

performance errors. 

Cell phone use is one of the most common distractions from driving that has been studied 

recently. In 1997, Redelmeier and Tibshirani reported in the New England Journal of Medicine 

that cell phone use quadrupled the risk of collision during the period of the call. Strayer, Drews, 

and Johnston (2003) did a series of experiments that showed that talking on a hands-free cell 

phone during driving causes what they label “inattentive blindness”. The experiments ranged 

from looking at driving performance errors to determining that drivers do not recall billboards 

that they fixated on while driving and talking on the cell phone. Direct Line Insurance (2000) 

has shown that reaction times for drivers averaged 30% slower when the driver was engaged in a 

cell phone conversation while driving than when the driver was legally over the limit for alcohol 

consumption and driving. Furthermore, the reaction times for drivers talking on a mobile phone 

were 50% slower than when they were driving without one. 

Driving and distraction is a large research area. Tijerina (2000) reports that predicting costs and 

benefits of the driver distraction associated with in-vehicle technology is very complex and 

difficult. However, driver behaviors and operational problems with the technology can be 

evaluated. There is no doubt that crash data and driver distraction are related. There are, 

however, so many variables that it is difficult to predict what level of distraction would cause an 

accident. Tijerina uses the analogy to smoking and lung cancer. You will not necessarily get 

cancer from smoking, but the risk is much greater. Similarly, you may not have a performance 

error if you are distracted while driving, but the risk of error is much higher. 
14 

5. Discussion and Future Work 

Validation of a model is an important process. A validation approach depends on the purpose of 

the model. It is completed “from the perspective of the intended use” (Department of the Army, 

1997). In this case, validation implies that the representation of driving is correct for 

determining the mental workload associated with driving tasks in a military combat vehicle. 

When one is comparing the model constructs with other driving models, this model includes all 

the components of human information processing included in the other models. There do not 

appear to be incongruencies. Additionally, all the studies investigated showed that mental 

workload was at or near the threshold during driving. This is consistent with the results of this 

driving model in both studies. This implies that additional mental workload would result in an 

increase in the potential for performance errors. Based on these comparisons, it is believed that 

this representation of driving is valid for the purpose that it is used, most recently vehicle crew 

workload analysis. 

Additional work is planned to further validate the driving model and further validate the finding 

that in a combat vehicle, the driver should not be required to perform additional tasks unless 

driving is fully and reliably automated. Two separate studies are planned. The first study is to 

use the driving tasks from this model to represent teleoperation. The driving tasks will not 

change but the workload will be different because of the modality and attentional demand of the 

task. The revised model will then be used in a “model-test-model” approach to predict operator 

performance during the teleoperation of an unmanned ground vehicle while the operator travels 

in a moving vehicle (Hill, Tauson, and Stochowiak, 2003). Model predictions of performance 

and test results will be compared and the model will be adjusted to better represent the actual 

teleoperation. The model output will then be validated with test results from ensuing study. 

The second study being considered is a validation of the workload threshold predicted by the 

model. This study will use an actual vehicle on an outdoor course. The driver will be required 

to operate the vehicle separately while completing secondary tasks. Secondary tasks will mimic 

typical tasks that are performed during driving, both in the civilian world and the military, 

talking on the radio, talking to other individuals, looking for hazard indicators, etc. The 

expectation is that each of these distractions will cause a decrease in performance. This study 

approach is still being defined and has not been formally proposed. 

This model appears to be an acceptable representation of driving for the purpose that it is being 

used. The results of the two studies should further validate the model. 

This work is valuable to the Army in the design of any vehicle but primarily, combat vehicles. 

Even if driving were automated, the visual and cognitive workload associated with monitoring or 

intervening in an autonomous mode would require that the operator be focused on driving alone 
15 

during some intervals. Although technological advances are promising, current technology 

requires the full attentional demand of the driver. This driving model is therefore an important 

component in the determination of the functional allocation between crew members in military 

vehicles. 
16 

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19 

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