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Making Driving Simulators More 

Useful for Behavioral Research—

Simulator Characteristics 

Comparison and Model-Based 

Transformation

The Exploratory Advanced Research Program

SUMMARY REPORT
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Notice

This document is disseminated in the interest of information exchange under the sponsorship of the Department of Transportation. 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 

and manufacturers’ names appear in this report only because they are essential to the 

object of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

government, industry, and the public in a manner that promotes public understanding. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, 

and integrity of its information. FHWA periodically reviews quality issues and adjusts its 

programs and processes to ensure continuous quality improvement.

Cover images show simulated roadway geometry developed by the University of Iowa (left), the National 

Advanced Driving Simulator (center), and the Federal Highway Administration’s motion-base driving 

simulator (right). ©The University of Iowa
Technical Report Documentation Page 

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Highway and traffic engineers face 

considerable challenges in creating designs 

that are consistent with drivers’ capabilities and expectations; however, failing to consider driver behavior can cost lives and millions 

of dollars if roadways require revision after they 

are built. The use of driving simulators to guide 

designs or to evaluate design choices is a promising approach, but discrepant results across studies undermine the utility of these findings. This is 

particularly true when simulator results fail to 

match on-road data. One potential source of this 

mismatch is when the simulator does not have 

the appropriate fidelity, or realism, to address the 

design issue of interest. Appropriate simulator 

fidelity, which includes the simulator hardware 

and software as well as the modeling of the virtual 

environment, is an important component of obtaining data useful for highway design. For example, 

one could envision a staged approach to simulator 

fidelity, similar to that used in software prototyping, in which a low-fidelity desktop simulator could 

be used for rendering scene and roadway elements, 

whereas a high-fidelity simulator could be used for 

speed estimates. Choosing the appropriate level 

of simulator fidelity to address a particular design 

issue represents a critical challenge.

The aim of this project was to address this 

challenge and to help engineers identify the appropriate simulator platform for particular design 

questions, as well as to identify a mathematical 

transformation that can equate simulator data to 

real-world outcomes. In particular, the research 

team identified highway design needs and matched 

them to specific simulator characteristics to facilitate the appropriate choice of simulator for a 

Executive Summary 

particular design problem. As part of this research, 

the research team developed and demonstrated a 

proof of concept approach to characterizing simulator fidelity to allow for comparison between 

simulators and the real world. The research team 

also developed a driving environment that contained virtual recreations of two roundabouts from 

Maryland and Arizona, as well as a gateway from a 

rural road to a small town in Iowa. The researchers 

manipulated this virtual environment to vary the 

visual complexity of the driving environment and 

tested it on four simulator platforms, three of 

which were tested with and without motion. They 

compared driver judgment of fidelity and performance across the simulator platforms. No consistent effect of motion was found, but a moderate 

effect of visual complexity was apparent in the 

data. Performance data showed good relative and 

absolute matches to on-road speed data. The 

researchers used the data to develop linear regression and process models that could be used to 

transform the simulator data to match the on-road 

data. These models will provide the foundation for 

future work that will allow designers to transform 

results for simulator studies to make design decisions and to predict changes in driver behavior and 

performance on the basis of evaluations conducted 

on simulators. For example, these models can relate 

speed through a roundabout observed in a simulator to speed that is likely to be observed on the road.

Following completion of this project, additional 

work is necessary to improve and refine the tools 

developed so far. One area that requires refinement is the characterization of simulators. This is 

because those characteristics that matter most are 

not always the easiest to measure. Additional work 

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is needed to define the critical measures that 

differentiate simulator fidelity related to roadway 

design. Additional work is also needed to characterize what constitutes a typical vehicle and how 

much variability exists among vehicles on critical 

measures. These data could then be used to 

enhance the psychophysical scaling required to 

determine when a simulator is noticeably different 

from a typical vehicle and the extent to which different vehicle types influence highway design 

decisions. These differences must also be investigated to determine whether future studies need to 

include not only a range of drivers, but also a range 

of vehicle types.

This research would also be enhanced through 

its application to real-world design problems to 

provide the opportunity for continued evaluation 

and refinement. For example, use of a simulator to 

support a State Department of Transportation 

project, from inception to evaluation, would enable 

a thorough evaluation of the utility of the simulator 

in all phases of the design process. Through final 

evaluation of the real-world design implementation, the predictions of the simulator across a 

broader range of performance metrics could be 

assessed, and model refinements could be made. 

Another promising line of research would be to 

draw on naturalistic data to identify critical design 

issues and scenarios that can be further examined 

through simulator studies. These studies would 

provide additional data to improve the transformations of simulator to real-world data. A further 

opportunity would be to examine the minimum 

fidelity of simulator needed at each phase of the 

design process and across design problems. If 

lower fidelity simulators can be used to successfully address design decisions, then their use may 

be opened up to a broader group of highway 

designers who cannot necessarily afford more 

expensive simulation platforms.

The model-based transformations used in this 

study highlight the promise of driver modeling in 

helping to address highway design decisions. 

Ongoing projects continue to explore the use of 

driver models to enhance driver safety through a 

systematic evaluation of design options; however, 

this requires a reliable and validated model of the 

driver. Additional work along these lines is therefore needed, particularly as it relates to roadway 

geometry and visual complexity. These theorybased models can be used to accumulate an understanding of simulators and driver behavior related 

to a set of stimuli. A comprehensive approach that 

integrates a driver model with the Interactive 

Highway Safety Design Model would provide benefits to highway designers as an efficient way of 

using previous data to assess new design decisions.

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Table of Contents

INTRODUCTION 1

Improving Understanding 1

Understanding Physical and Behavioral Fidelity 2

Comparing Simulators and Scenarios 2

PHYSICAL FIDELITY 4

Study Simulators 4

Supporting a Realistic Driving Simulator 5

Measuring Levels of Realism 5

BEHAVIORAL FIDELITY 6

Data Collection 6

Simulator Evaluation 7

Influencing Driver Behavior 7

MODEL-BASED TRANSFORMATION OF SIMULATOR DATA 8

Predicting On-Road Behavior 8

Analyzing Models 9

Analyzing Scenarios 9

CONCLUSIONS AND RECOMMENDATIONS 10

Future Research 11

ADDITIONAL INFORMATION 13

Simulator Characteristics Study 13

Simulator Descriptions 13

Summary of Characteristics 13

Measurement Protocol for Characterizing Simulators 13

Participant Forms and Questionnaires 13

Contact Information 13

REFERENCES 15
List of Figures 

Figure 1. The National Advanced Driving Simulator motion-base driving simulator. 4

Figure 2. The Federal Highway Administration motion-base driving simulator. 4

Figure 3. The Western Transportation Institute Simulator. 5

Figure 4. The National Advanced Driving Simulator miniSim Simulator. 5

Figure 5. Example of geometric matching of real (top) and simulated (bottom) 

roadway geometry of a gateway in Iowa. 6

Figure 6. An overview and screen image of the practice driving route. 7

Figure 7. Layout of the first Arizona roundabout. 9

Figure 8. Layout curvature data of the first Arizona roundabout. (The width of the 

line in the center graph corresponds to the radius of curvature shown in the 

bottom graph.) 9

Figure 9. Layout of the first Maryland roundabout. 9

Figure 10. Layout curvature data of the first Maryland roundabout. (The width of the 

line in the center graph corresponds to the radius of curvature shown in the 

bottom graph.) 9

List of Acronyms and Abbreviations 

EAR Exploratory Advanced Research

FHWA Federal Highway Administration

NADS National Advanced Driving Simulator

WTI Western Transportation Institute

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This report summarizes the results of a Federal Highway Administration 

(FHWA) Exploratory Advanced Research (EAR) Program-funded research 

project that explored the challenges of using driving simulators to guide 

roadway designs and evaluate design choices. The aim of this project was to help 

engineers identify the appropriate simulator platform for particular design questions, as well as to identify a mathematical transformation that can equate simulator 

data to real-world outcomes. 

Highway and traffic engineers face considerable challenges in creating designs 

that are consistent with drivers’ capabilities, expectations, and limits.1 Drivers often 

behave in complex and counterintuitive ways, and failing to consider driver behavior 

can cost lives and millions of dollars if roadways require revision after they are 

built.2,3 Driving simulators provide a promising approach to addressing this challenge because they make it possible to visualize new roadway designs as well 

as safely expose drivers to demanding situations without the expense of fully 

implementing the design.4 Driving simulators also provide a means of conveying 

road design concepts to stakeholders through visualization and have the potential 

to be an important part of policy decisions and public acceptance.5,6

Improving Understanding

There have been many recent advances in simulation technology, which has led to 

a wide range of driving simulators available to researchers. These simulators all 

offer different levels of realism, known as fidelity, in addition to varying levels of 

complexity and usage costs. Such diversity makes it difficult for researchers to 

know which simulator is appropriate to address a given design question. This 

uncertainty is thought to be one reason why simulators have not been more widely 

used by highway and traffic engineers.7

An improved understanding of the varying characteristics of simulators and 

how well they might reproduce driver behavior would make driving simulators far 

more useful for engineers. The ideal situation would be for simulator characteristics to exactly match actual cars and roadways, but this is beyond the capabilities of 

even the most advanced simulators at this time. Instead, the goal is to minimize the 

differences between the physical characteristics of the simulator and the roadway 

and therefore ultimately minimize the difference between behavior observed in the 

simulator and out on the road. 

Introduction
Understanding Physical and Behavioral Fidelity

In addition to physical differences, there are several other factors known to affect driver 

behavior that can prove difficult or impossible to simulate, including a driver’s motivation 

for the trip or the real-world consequences of a crash. Matching the physical features of the 

simulator to the roadway experience, known as physical fidelity, is therefore just one condition that must be replicated to ensure that driver behavior in the simulator matches behavior observed on the road.8 Until now, the driving simulator community has mostly focused 

on gross measures of physical fidelity, such as “high” or “low” fidelity. The next step, and 

broader research goal, is to match the behavior of drivers in the simulator with behavior on 

the road, known as behavioral fidelity.

4 This requires sufficient realism of simulator controls 

and vehicle-handling characteristics to match actual vehicle performance.9 The goal is for 

behavior in the simulator to match behavior on the road accurately enough to support 

design decisions. 

Simulator fidelity is further complicated by the fact that physical and behavioral fidelity 

are related to each other, in that imperfect physical fidelity will lead to imperfect behavioral 

fidelity.8 Despite this, imperfect fidelity is still often sufficient to support roadway design 

decisions. For example, a simulator might fail to accurately replicate the cues required to 

guide behavior, possibly leading drivers to drive faster than they would on the actual road.10,11

Drivers, however, rely on multiple interchangeable cues to guide behavior and can substitute 

one set of cues for another.12,13 This means that two different simulators might still produce 

similar behavior results because drivers can adapt and use the available cues within each 

simulator.14,15 Simulator fidelity is further influenced by the level of information a simulator 

might provide for one task compared with another. A simulator might offer a high level of 

realism, or fidelity, for one set of tasks but only a medium level of realism for another. For 

example, a simulator may offer highly accurate renderings of road signs for a sign-reading 

task yet would be classed as a low-fidelity simulator for driving that involves 90-degree turns 

because it fails to provide a preview of the road on which drivers rely during the turns.4

Comparing Simulators and Scenarios

During this project, the research team explored task-dependent fidelity and examined the 

difference between physical and behavioral fidelity. The following summary compares 

behavior across four simulator platforms with four different configurations of motion base 

and visual complexity. The simulators were used to analyze a total of six roadway scenarios 

among them, comprised of four roundabouts and two gateways. This summary includes a 

description of the different driving simulators used in the project, a description of physical 

fidelity in terms of the cues drivers use for vehicle control, an assessment of the behavioral 

fidelity of these simulators, and an overview of a model developed as part of the project to 

relate simulator behavioral data collected in a driving simulator to data collected on the road.

2
Simulators and Scenarios
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Figure 1. The National Advanced Driving Simulator 

motion-base driving simulator. 

Study Simulators

The following section provides a brief overview of 

each of the four simulators used in this study.

National Advanced Driving Simulator

The NADS used a 1998 Chevrolet Malibu cab 

mounted on a motion base with 13 degrees of freedom, as shown in figure 1. Accelerator and brake 

pedals used software-controlled electrical motors 

to provide feedback. The simulator has a 360-degree 

visual display system consisting of eight projectors 

that project visual imagery inside the dome, and 

scenery is updated at 60 Hz. The NADS features the 

ability to swap among several types of vehicle cabs.

Federal Highway Administration Highway 

Driving Simulator

The FHWA Highway Driving Simulator, shown in 

figure 2, is composed of a full 1998 Saturn vehicle 

cab mounted on a motion base with 3 degrees of 

freedom. The FHWA simulator has a 240-degree 

visual display system consisting of five projectors 

Physical Fidelity

Physical fidelity relates to the degree to which the simulator replicates the physical properties of the 

driving situation, unlike behavioral fidelity, which is associated with the simulators’ ability to replicate 

behavior observed in the world. This study’s research team examined four simulators representing a 

broad range of simulation capability and fidelity and measured characteristics for each. Simulators 

included in the study were the National Advanced Driving Simulator (NADS), the FHWA Highway 

Driving Simulator, the Western Transportation Institute (WTI) Simulator, and the NADS miniSim.

that project onto a cylindrical screen that is 2.7 m 

(9 ft) tall. All scenery is updated at 60 Hz.

Western Transportation Institute Simulator

The WTI simulator, shown in figure 3, consisted 

of a 2009 Chevrolet Impala sedan mounted on a 

motion platform with 6 degrees of freedom. The 

WTI simulator has a 240-degree forward-field-ofview system augmented by a 60-degree rear-view 

display system, consisting of five projectors and a 

curved screen in front of the driver and a single 

projector and a flat screen behind the driver. Sideview mirrors with digital screens also portrayed 

the scenarios for a total of eight visual channels.

National Advanced Driving Simulator 

miniSim Simulator

The NADS miniSim, shown in figure 4, is a portable, lower cost simulator that runs software similar 

to the NADS simulator. The miniSim has no motion 

base and, for this study, featured a quarter-cab configuration with a seat and steering wheel from an 

Figure 2. The Federal Highway Administration 

motion-base driving simulator. 

©The University of Iowa©The University of Iowa
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actual vehicle. It has three flat-panel plasma displays and projects the image of a rear-view mirror 

on the center plasma display.

Supporting a Realistic Driving Simulator

Simulators are often characterized by a set of 

features that describe their hardware components, 

including driving controls, screens, resolution, and 

mirrors. The hardware configuration is critical for 

conveying information to the driver, such as speed 

and curve geometry and gas and brake pedal force; 

however, although hardware is a necessary condition for high behavioral fidelity, it is not sufficient 

on its own. For a driving simulator to accurately 

convey the driving environment, it must depend on 

hardware and software. In fact, software is often 

more important than is hardware because it is the 

software controlling what is presented to the driver. 

Working together, the hardware and software 

generate signals for the driver and influence how 

they perceive the environment and control the 

state of the vehicle relative to the environment. 

There are three key requirements for supporting a 

realistic driving simulator: (1) perception of distances, speed, and time to reach relevant objects in 

the real world; (2) control of the car’s speed and 

direction through control inputs; and (3) vehicle 

response to the control inputs.12,16,17 The ability to 

identify and measure simulator characteristics in 

relation to these requirements enables researchers 

to define important differences between simulators, 

even if their hardware specifications are identical. 

In addition to taking a sample of measurements 

to quantify the physical fidelity of the driving 

simulators used in this study, the research team 

aimed to relate each simulator characteristic to 

what drivers experience on the road to assess how 

characteristics might affect behavioral fidelity.

Measuring Levels of Realism

Following data collection and assessment of 

simulator characteristics, the NADS and WTI simulators showed the highest level of physical fidelity; 

however, study results indicated that no single metric can serve as a proxy for overall simulator fidelity. 

In fact, the broad concept of overall level of fidelity is 

in fact misleading and should instead be addressed 

in a multidimensional manner. Several issues must 

be addressed before this multidimensional approach 

is applied more broadly. For example, cars differ 

substantially across most of the simulator characteristics measured. It is therefore important to identify 

which differences are important and which are not. 

Drivers were also shown to easily adapt to a wide 

range of vehicle characteristics, including maximum 

acceleration and deceleration, steering inputs, pedal 

feel, and visual contrast. Even though a driver might 

perceive differences between a simulator and the 

car on the road, the difference might not influence 

driver behavior but may still result in differences in 

workload and driver strategies in obtaining the same 

driving performance. Following analysis of various 

metrics of physical fidelity, further research is 

needed to quantify the variation of simulator characteristics, the degree to which drivers can adapt to 

different vehicle simulator characteristics, and the 

degree to which these characteristics influence 

behavior, driving strategies, and operator workload.

©The University of Iowa©The University of Iowa

Figure 3. The Western Transportation Institute 

Simulator.

Figure 4. The National Advanced Driving Simulator 

miniSim Simulator.
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Behavioral Fidelity

Behavioral fidelity refers to the simulators’ ability to replicate driver behavior observed in the real world 

and is considered the ultimate measure of simulator fidelity. Researchers for this project collected 

simulator-based data and on-road data to describe behavioral fidelity for each of the four simulators 

used in the study. They collected data both with and without the simulators’ motion base engaged and 

with and without a more complex visual scene.

Data Collection

The analysis involved 167 participants ranging in 

age from 25 to 45 years. Forty-eight people each 

participated in the WTI, FHWA, and NADS simulators, and 23 people participated in the miniSim. 

The simulators operated on three different software platforms, but all used the same scenarios. 

Scenarios were used that involved two types of 

road segments, roundabout and gateway. The term 

gateway refers to a transition from a rural road into 

a town. In this study, the gateway was designed to 

achieve a 40-km/h (25-mi/h) speed limit on a twolane suburban roadway in Iowa by using converging pavement markings, narrow lane markings, and 

speed advisories, as shown in figure 5. The roundabout scenarios were located on a rural two-lane 

arterial highway adjacent to the overpass of a major 

four-lane highway in Maryland, and a sequence of 

two roundabouts located on a rural two-lane roadway connected with a two-lane frontage road 

adjacent to an interstate highway in Arizona.18,19

The research team selected these roadway 

elements based on discussions they had with FHWA 

about the potential application of driving simulators to investigate design issues.7

 The researchers 

selected real-world examples of each road segment 

based on the availability of spot-speed data (i.e., the 

instantaneous speeds of vehicles at specific spots of 

the roadway) from published reports, and they 

based the virtual environment reproductions on the 

engineering schematics available for each site. The 

goal of these reproductions was to duplicate the 

road segment geometry and road features visible to 

the driver that were important to navigating the 

road segment; however, implementing identical 

scenarios on four different simulator platforms 

presented unique challenges and required a significant amount of fine tuning. A lack of established 

standards for road networks and dynamic element 

scripts was identified as a key impediment to sharing 

data on the driving environments across simulators 

as an efficient and smooth process.

Figure 5. Example of geometric matching of real (top) and simulated (bottom) 

roadway geometry of a gateway in Iowa.

©The University of Iowa
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The researchers used the same general procedure 

to govern data collection at each simulator facility, 

although minor variations were introduced 

depending on the logistical and operating requirements of each site. They used existing databases 

and local advertisements to recruit participants, 

who were initially contacted and verbally screened 

for eligibility and motion sickness before moving 

forward. Participants initially conducted a practice 

drive in the simulator to familiarize themselves 

with the operation of the simulator vehicle and 

experience of driving within the virtual environment, as shown in figure 6. Participants then drove 

the main route twice with varying levels of visual 

complexity and road segment orders. 

Simulator Evaluation

Each participant then completed a simulator realism 

survey to evaluate the overall feel, braking response, 

and visual realism of the simulator. These components formed the three dimensions of subjective 

simulator fidelity, and the results showed that the 

simulators differ considerably over these three

dimensions of simulator realism. The NADS simulator, with the advanced motion base on, had the 

highest reported realism of overall feel. The FHWA 

simulator represented the lowest realism of overall 

feel; however, this simulator did provide the highest 

perceived realism for being able to read the signs 

and see the road. This was attributed to the simulator being specifically developed to offer visual 

properties to support research for road and signage 

design. Drivers felt best able to brake and stop with 

NADS while having the motion on and with the 

WTI while having the motion off. This part of the 

study showed that no simulator configuration dominates the other in terms of perceived realism. In 

summary, the participants judged that the NADS is 

the most realistic simulator overall, the FHWA simulator best supports drivers’ ability to read signs, and 

the WTI simulator provides the best braking feel. 

Moreover, the effect of the motion base was shown 

to be relatively small and can even have a negative 

impact on realism.

Influencing Driver Behavior

As part of this research project, the research team 

analyzed the effects of simulator, motion, and visual 

complexity on driver behavior. The effect of motion 

failed to reach statistical significance and had little 

impact; however, visual complexity had a substantial 

influence on behavior. In some cases, particularly for 

drivers in the miniSim in the Iowa gateway, it led to 

an approximately 8-km/h (5-mi/h) speed reduction. 

In addition, drivers of the miniSim on the Maryland 

roundabout were traveling almost 16-km/h (10-mi/h) 

faster than were drivers in the other simulators. In 

most other cases, the influence of visual complexity 

was modest, so the substantial simulator differences 

between the miniSim and the other simulators is 

thought to have contributed to this speed difference.

In general, the collected data for the mean speed 

of drivers in the simulators relative to the mean speed 

observed on the road was similar. Drivers in the 

WTI simulator drove closest to real-world speeds at 

low speeds, whereas drivers in the FHWA simulator 

drove substantially slower at speeds below 50 km/h 

(31 mi/h) but drove faster once speeds increased 

beyond that threshold. Results indicated that speed 

varies more in the simulator than on the road. One 

explanation for this is that the simulator provides 

poorer cues regarding the speed and poorer feedback 

regarding drivers’ modulation of speed, leading to 

greater reliance on the speedometer, poorer speed 

control, and more variability in speed. This suggests 

that simulators can provide good estimates of the 

mean speed but poorer estimates of other elements of 

the speed distribution. Drivers drove faster and more 

variably in the miniSim and more slowly in the NADS 

relative to the speeds observed on the road, suggesting that the breadth of distribution may be more 

indicative of simulator fidelity than the mean speed.

Figure 6. An overview and screen image of the practice driving route.

©The University of Iowa
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Model-Based Transformation of Simulator Data

As described in the previous section, behaviors seen in the driving simulator and on the roadway in this 

research project are generally in agreement, but there are still some mismatches to be addressed. For 

example, the distribution of speeds observed in the simulator should ideally match speeds observed on the 

road, but this is not always the case. Simulator characteristics can explain some of the behavior differences, 

but there are other important characteristics that can lead to differences, including familiarity with the 

route and individual driver motivations.

Predicting On-Road Behavior

Although the mean speed observed on the road 

compared with the simulator was shown to differ 

slightly in absolute terms during this study, in relative terms the mean speed was similar.8,20 To solve 

many design issues, it is important to match simulator and road data in absolute terms. There is a 

requirement to develop a method to transform simulator data to accurately predict on-road behavior. 

To meet this requirement, the research team 

focused on a computational model based on the 

perceptual, cognitive, and motor-control processes 

that govern driver speed maintenance. Unlike a 

regression model, this model uses a theoretical 

approach to explain the underlying constraints 

that bound the driver response and provides a way 

to transform the distribution of speeds observed in 

the simulator to those observed on the road. This 

approach offers promise in transforming simulator 

data to match roadway data and builds on existing 

models of driver behavior to describe how drivers 

perceive and respond to road characteristics. 

Analyzing Models

This project also included an analysis of applicable 

driver models to the roundabout scenarios and 

integration of these models into a simple speed 

maintenance model. The research team used data 

from NADS and the miniSim to estimate the parameters of the model—these simulators were chosen 

because of their differing configurations. Past 

research efforts beyond this project to model drivers’ speed choice and improve highway safety and 

capacity have already produced a series of models 

that predict drivers’ speed as a function of roadway 

geometry.21,22 For over 50 years, this has been an 

ongoing and active research topic, and yet there is 

still not a definitive model.23,24 Most of these existing statistical models summarize drivers’ speed 

choice without describing the mechanisms that 

guide speed selection.25

Process models, however, complement these 

statistical models and describe the drivers’ 

perceptual, decisionmaking, and motor control 

processes.10,26 Process models can describe drivers’ 

speed selection in terms of an error-correcting 

mechanism that strives to minimize the deviation 

from a desired speed. Desired speed might reflect a 

driver’s ability to steer the vehicle through a curve 

while maintaining an appropriate distance from the 

lane boundary, often expressed as the time-toline crossing.27 Though not part of this research 

study, one factor that influences this speed choice is 

traffic in the opposite lane. Without traffic, the 

research team noted that drivers often cross the 

centerline to smooth the curve, but most models 

assume the driver attempts to stay within the lane 

boundaries. Steering demand increases as the radius 

of the curvature and lane width decrease and speed 

increases. Small steering errors at high speed quickly 

lead a driver toward the lane boundary and forces 

drivers to slow down to maintain a constant time-toline crossing. This creates an inverse relationship 

between lateral acceleration and curve radius and 

can explain why drivers often choose speeds through 

curves that are less than what might be expected.12

Analyzing Scenarios

The research team focused its analysis on four of 

the six road segments considered in the earlier 

simulator data collections. These were the two 

roundabout sections from Arizona and Maryland. 
9

Two of these sections are featured in figures 7–10. 

This group of figures shows a bird’s eye view of 

the route through the roundabout, with each stage 

of the route annotated; and below this, the curvature across the segment is shown, also with each 

stage annotated. The curvature data from these 

sections were used as the primary input to the 

driver model, which determines the speed maintenance behavior of the driver model.

Model-based transformations aim to directly 

relate driver behavior observed in the simulator to 

behavior on the actual roadway. A transformation 

is made possible by first estimating those model 

parameters that produce the speed trajectories 

observed on the road; however, the limited on-road 

data available for this project make such estimates 

difficult. The researchers estimated the model 

Figure 7. Layout of the first Arizona roundabout.

Figure 8. Layout curvature data of the first Arizona 

roundabout. (The width of the line in the center graph 

corresponds to the radius of curvature shown in the 

bottom graph.)

Figure 9. Layout of the first Maryland roundabout.

Figure 10. Layout curvature data of the first Maryland 

roundabout. (The width of the line in the center graph 

corresponds to the radius of curvature shown in the 

bottom graph.).

©The University of Iowa©The University of Iowa

©The University of Iowa

parameters for each simulator configuration based 

on the data collected from the simulators. The 

resulting parameters reflected how simulator 

characteristics influence driver behavior. The model 

can then transform the simulator and estimate 

roadway data by adjusting the model parameters 

using a scaling parameter based on the ratio of the 

roadway parameter to the simulator parameter. 

This method is subsequently able to describe 

differences in how drivers negotiate curves through 

parameters of a dynamic driver model. These 

parameters can provide a more diagnostic and 

generalizable description of how driving in the 

simulator differs from driving on the road. The 

resulting practical utility of this contribution is that 

it can be used to transform speed observed in the 

simulator to speed observed on the road.

©The University of Iowa
10

Conclusions and Recommendations

As described in the previous section, behaviors seen in the driving simulator and on the roadway in this 

research project are generally in agreement, but there are still some mismatches to be addressed. For 

example, the distribution of speeds observed in the simulator should ideally match speeds observed on the 

road, but this is not always the case. Simulator characteristics can explain some of the behavior differences, 

but there are other important characteristics that can lead to differences, including familiarity with the 

route and individual driver motivations.

A central challenge in making simulators useful for 

roadway design concerns how well driver behavior 

in the simulator matches driver behavior on the road. 

The results of this study begin to address the question of how simulators can support highway and 

traffic engineers. Overall, the results show that simulators with high physical fidelity demonstrate high 

behavioral fidelity and are likely to provide good 

estimates of mean speeds in typical engineering 

applications, such as roundabouts and roadway 

treatments designed to moderate drivers’ speed. The 

use of the data from simulator studies can also be 

further refined through the use of the transformations developed as part of this research. The detailed 

analysis of both physical and behavioral fidelity 

included in this study suggests important opportunities to improve simulator fidelity and the need to 

carefully assess the match between simulator features and the properties of the roadway design issue.

In general, the NADS and WTI simulators 

showed the highest level of fidelity across the range 

of metrics examined; however, results also showed 

that no single metric can serve as a proxy for overall simulator fidelity. This illustrates how simulators can differ across different dimensions that 

affect the level of fidelity. The broad concept of an 

overall level of fidelity is also misleading and should 

instead be addressed in a multidimensional manner. 

In addition, there is a need, when considering 

fidelity, to consider the type of vehicle the simulator 

is designed to reproduce and the type of measure that 

is relevant in a given scenario. For example, the effect 

of using a motion base was minimal in the scenarios 

used in this study because there were few occasions 

of strong lateral or longitudinal g-forces. Indeed, 

the effect of simulator platform (e.g., miniSim versus 

NADS) was often less influential than was the 

effect of the details included in creating the virtual 

roadway (e.g., visual complexity).

The results of this research project confirm the 

importance of understanding how different dimensions of physical fidelity work together to provide 

overall fidelity. As noted earlier in this summary, 

even without perfect fidelity, drivers adapt and make 

use of the cues available to respond to the changes in 

the driving environment. Fewer cues for speed estimation may lead simulator drivers to attend to the 

speedometer more than they would on the road. 

Some of these differences in physical fidelity can 

degrade driver response to the point that behavioral 

fidelity is compromised, whereas in other cases 

drivers adapt compensating behaviors that allow for 

realistic responses but may not fully reflect how the 

driver would respond in the real world. Attending to 

the speedometer to maintain the instructed speed 

may distort how drivers respond to other elements 

of the roadway. These cases therefore require care 

when interpreting the results. 

The interaction between physical fidelity levels 

and resulting behavioral fidelity also needs to be 

considered. First and foremost, the driver experiences the simulator software through the visual 

display, motion system, sound system, steering 

torques, and pedal forces. If the steering forces are 

not produced by the vehicle dynamics model, then 

the vehicle dynamics that the driver experiences 

will differ substantially from the true one. In a 

similar vein, if the simulator does not present vestibular cues, then much of the vehicle dynamics 

(accelerations) will not be perceived directly by 

the fast vestibular system but instead through 

slow visually perceived speed changes; therefore, 

drivers will perceive dynamics as much more 

sluggish than what the software might portray. 
11

This research effort provides a valuable 

contribution to the understanding of the use of 

simulators for evaluating roadway designs. Prior 

efforts have focused on addressing research design 

projects on single platforms in a fixed configuration 

and have failed to address the discrepancies among 

outcomes on different platforms. This project 

directly addresses those issues to provide guidance 

to the research community and highway designers. 

The research shows how the simulator configuration 

affects speed in the simulator relative to the real 

worldreal world. In addition, it shows that modelbased transformations can be used to estimate 

speed adjustments based on the simulator configurations for the platforms tested and can potentially 

be extrapolated to other configurations. The results 

show that using a high-fidelity simulator, such as 

the NADS, FHWA, or WTI simulator, with attention 

to accurately rendering the visual complexity of the 

roadway, will lead drivers in the simulator to drive 

at speeds quite comparable to those observed on 

actual roadways. 

Overall, the research team for this project 

developed a set of tools that provide the foundation 

for future work that allows designers to transform 

results for simulator studies to make design decisions and to predict changes in driver behavior and 

performance based on evaluations conducted on 

simulators. This project is an important first step in 

understanding not only the translation of simulator 

data to real-world contexts, but also the hidden and 

complex issues that underlie this type of study, 

comparing multiple simulators with each other 

and with a real-world data set. Some of the key 

contributions of this project include the following:

•	 Proposed set of metrics and methods to

characterize the physical fidelity of simulators.

•	 Proposed set of analytic methods and graphics to

characterize the behavioral fidelity of simulators.

•	 Identification of simulator characteristics that

may be most relevant to measuring speed in 

road design (e.g., visual complexity, field of view, 

and motion base).

•	 Linear model specific to each simulator to predict

mean speed and speed variance for real-world 

context (for the scenarios selected).

•	 Driver model specific to each simulator to

transform curve negation speed in the simulator 

to the real-world curve context.

Future Research

Understanding Real-World Values

The metrics and methods used in this study to 

characterize physical fidelity of simulators highlight a need to consider differences among actual 

vehicles in assessing simulator fidelity. When 

considering differences between real-world cues 

and those provided by the simulator, a more extensive survey of typical values of vehicle characteristics should be conducted, as it was revealed that 

parameters can vary from vehicle to vehicle in the 

same class. Understanding the range of possible 

real-world values will enable more accurate descriptions of simulator vehicles relative to their realworld counterparts than is possible when a single 

vehicle is chosen to represent a class of vehicles. 

Normalizing for these differences in variation 

could greatly improve the mapping from simulator 

characteristics to physical and behavioral fidelity.

Tuning Vehicle Characteristics

Looking to the future, a specific research direction 

to address vehicle characteristics could investigate 

methods to tune simulators with respect to a 

standard vehicle or a generic compiled vehicle (e.g., 

based on the average of a set of typical vehicles) or 

develop methods to quickly adjust tuning parameters to the type of vehicle (and expected “feel”) of a 

given driver. In addition, the approach used in this 

report could be expanded to include a wider range 

of typical vehicles, rather than a single example.

Considering Virtual Fidelity

When describing overall simulator fidelity, weighting 

the factors that can be easily described provides 

insight into how simulators compare to each other 

and the real world; however, this does not provide 

a comprehensive understanding, because not all 

features that matter are easily measured and not 

all features contribute equally. When simulators 

are considered relative to a specific type of evaluation, weighting the factors that contribute based on 
12

their influence on the behavior of interest is a 

better approach to understanding the relative fidelity to address research questions. This would mean 

that a simulator might be a “high” fidelity simulator 

for accessing speed through a roadway design but 

a “low” fidelity simulator for another design 

problem. The results of this project point to the 

importance of considering the fidelity of the virtual 

environment in this assessment—visual complexity 

had a larger effect than the motion base. Along 

these lines, the presence of traffic might have a 

surprisingly strong effect on driver behavior.

Quantifying Simulator Characteristics

Further research is needed to quantify the variation of simulator characteristics, the degree to 

which drivers can adapt to different vehicle simulator characteristics, and the degree to which these 

characteristics influence behavior as well as driving strategies and operator workload. In addition, 

a larger number of simulator configurations should 

be compared to make the mapping from simulator 

characteristics to behavior tractable. To achieve 

this, researchers will need to develop focused 

studies that explore which simulator characteristics 

humans can adapt to and which distort behavior. 

Based on the results of this study, systematic 

variation of simulator features, such as visual 

complexity, as well as sound and vibration might 

be particularly fruitful. 

Collecting Additional Real-World Data

To further understand the comparison between 

the simulator and the real world, additional realworld data is required to provide a more continuous and complete description of driver behavior. 

This project was limited to speed observed on the 

road at widely spaced points, and the current onroad data are sparse with no lane position data. 

Continuous speed data along with accelerator 

pedal modulation and lane-position data would 

provide a much richer basis for comparing behavior in the simulator to that observed on the road. 

Collecting instrumented vehicle data in 3 segments 

by using 15 subjects per condition so that a model 

can be developed would provide a strong foundation for continuing this research. 

Examining Naturalistic Data

Naturalistic data provide another promising 

avenue for future research. Naturalistic data 

associated with crash and near-crash situations 

observed on the road could be replicated in the 

simulator where a more detailed assessment of 

driver behavior and potential countermeasures 

would be possible. This would provide a more comprehensive basis for using driving simulators to 

enhance traffic flow and also improve road safety. 

Rather than a focus on replicating speed observed 

on the road, the focus could be on replicating in the 

simulator the behavior that precipitates crashes.
13

Additional Information

The full final report of this project, Making Driving Simulators More Useful for Behavioral Research—

Simulator Characteristics Comparison and Model-Based Transformation (October 2013), is available at: 

www.nads-sc.uiowa.edu/publicationStorage/20131399331159.N2013-016_Making%20driving%20

simul.pdf. 

The full report also includes the following additional information:

Simulator Characteristics Study 

The researchers surveyed organizations that 

operate driving simulators to identify a full range 

of characteristics, capabilities, and limitations 

found in a representative sample of driving simulators. The survey evaluated a range of simulators 

from desktop to full-vehicle simulators using a 

variety of displays and controls. 

The survey included a brief, informal literature 

review to ascertain the state of the technology 

and current practices in driving simulation. The 

researchers subsequently developed a survey of 

simulator characteristics based on the results of the 

review and input from subject matter experts in 

the field of driving simulation.

Simulator Descriptions

The full report provides a high-level description 

for each of the simulators included in the survey. 

These descriptions provide a broad overview of the 

distinguishing characteristics of each simulator 

system. Features listed include type of cab, visual 

display characteristics, motion and haptic capabilities, and audio capabilities.

Summary of Characteristics

The full report includes a summary of the primary 

characteristics of the driving simulators. This is 

intended to provide a basis for comparing the 

features that are likely to be most important to the 

goals of the project. A table lists characteristics that 

are related to dimensions of fidelity that will likely 

affect driver performance.

Measurement Protocol for 

Characterizing Simulators

The researchers made measurements of the visual, 

motion, vibration, haptics, tactile, and sound cues 

in each of the four simulators. They included 

specific measurements in the final report to assure 

that it can accurately quantify the degree to which 

a simulator satisfies the simulator perceptualcontrol requirements. 

Participant Forms and Questionnaires

The full report includes a copy of the motion sickness 

screening form and motion sickness and simulator 

realism questionnaires provided to participants.

Contact Information

For more information on this project, contact 

Brian Philips at FHWA, 202-493-3468 (email: 

brian.philips@dot.gov).
14
15

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Office of Safety Research and Development

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Publication No. FHWA-HRT-15-016

HRTM-30/03-15(WEB)E