DRIVERS BEHAVIOR WHEN DRIVING VEHICLES WITH OR WITHOUT ADVANCED DRIVER ASSISTANCE SYSTEMS

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Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
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Drivers’ behavior when driving vehicles with or without advanced driver 
assistance systems: A driver simulator-based study 
Raghuveer Gouribhatla a, Srinivas S. Pulugurtha b,* 
a Infrastructure and Environmental Systems (INES) Ph.D. Program, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223- 
0001, USA 
b Infrastructure, Design, Environment, and Sustainability (IDEAS) Center, Civil & Environmental Engineering Department, The University of North Carolina at Charlotte, 
9201 University City Boulevard, Charlotte, NC 28223-0001, USA 
A R T I C L E I N F O 
Keywords: 
Driving 
Simulator 
Advance driver assistance systems 
ADAS 
A B S T R A C T 
Automotive companies have been developing vehicles with advanced features that aid in various driving tasks. 
These features are aimed at enhancing safety by either warning the drivers of a potential hazard or picking up 
certain driving maneuvers like maintaining the lane or a constant headway. They are part of vehicles with driver 
assistance technology and are vital for the successful deployment of connected and automated vehicles in the 
future. However, drivers’ behavioral response when driving vehicles with such advanced driver assistance sys-
tems (ADAS) compared to vehicles without ADAS may vary and is meagerly explored. This research evaluates 
drivers’ behavioral response to scenarios when driving vehicles with ADAS like lane departure warning (LDW), 
blind spot warning (BSW), and over speed warning (OSW) compared to vehicles without ADAS. Rural, urban and 
freeway driving scenarios were developed in a driver simulator and tested on 43 participants aged sixteen years 
to sixty-five years. The results show that ADAS influence driving behavior by making drivers less aggressive and 
harmonizing the driving environment. The influence of ADAS on the driver behavior was different in rural, 
urban, and freeway driving scenarios. The drivers’ behavioral response to scenarios varied with the lighting and 
weather condition as well as with the age, gender, and ethnicity of the participant. While ADAS help by reducing 
lane departures or speeding and enhance safety, an indirect influence on braking, turning, and car-following 
were also observed. The findings help assess driver behavior when driving vehicles with advanced features 
and build better systems. 
1. Introduction 
Per the Association for Safe International Road Travel (ASIRT), more 
than 38,000 traffic crash related fatalities are reported every year in the 
United States (ASIRT, 2019). They are the leading cause of death among 
people up to 54 years in age (ASIRT, 2019). Additionally, an estimated 
$380 million was lost in direct medical bills while economic impacts of 
the crashes, be it direct or indirect, accounted for about $871 million 
(ASIRT, 2019). Traffic safety problems are further influenced by newer 
vehicles that are added to the roads every year, with more than 17.6 
million passenger cars and trucks sold in 2016 alone (Garcia, 2017). Per 
the Alternative Fuels Data Center (AFDC), an estimated 3.21 trillion 
vehicle miles were traveled in 2018 in the United States (AFDC, 2019). 
Per the National Safety Council (NSC), driver errors are the leading 
cause of traffic crashes, contributing to about 94% of the crashes in the 
United States (NSC, 2020). The nature of driver errors varies widely and 
have been broadly classified into four types, namely, recognition errors, 
decision errors, performance errors, and non-performance errors (Bellis 
and Page, 2008). The recognition errors are common errors like wrongly 
estimating the distance or speed of the vehicle, and account for about 
41% of the crashes. The decision errors like over-speeding, following too 
closely or making illegal actions account for 34% of the crashes (Bellis 
and Page, 2008). The performance errors like losing control of the 
vehicle account for 10% of the crashes (Bellis and Page, 2008) while the 
non-performance issues like health account for about 7% of the crashes 
(Bellis and Page, 2008). 
Automotive companies are striving to enhance their vehicles to 
eliminate or minimize recognition, decision, and performance related 
driver errors and reduce the number of crashes. Various advanced driver 
assistance systems (ADAS) and automated features are designed to assist 
* Corresponding author. 
E-mail addresses: rgouribh@uncc.edu (R. Gouribhatla), sspulugurtha@uncc.edu (S.S. Pulugurtha). 
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https://doi.org/10.1016/j.trip.2022.100545 
Received 23 September 2021; Received in revised form 27 November 2021; Accepted 8 January 2022 
mailto:rgouribh@uncc.edu
mailto:sspulugurtha@uncc.edu
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or in some cases take over certain driving maneuvers. They include lane 
departure warning (LDW), blind spot warning (BSW), over speed 
warning (OSW), forward collision warning (FCW), lane keep assist 
(LKA), adaptive cruise control (ACC), and automated emergency 
braking (AEB). Each of these advanced features are focused at 
addressing a particular task of driving, thereby, reducing driver errors 
and enhancing safety. They are part of vehicles with driver assistance 
technology and are vital for the successful deployment of connected and 
automated vehicles in the future. While the advanced features warn or 
takes up certain driving tasks making a drivers’ job easier to some 
extent, the driver needs to be cautious at all times to take over driving as 
soon as any of these systems fail to react or disengage or are not suitable 
for automated driving. Per the Society of Automobile Engineers (SAE) 
International, drivers of level 1 (with at least one vehicle control func-
tion), level 2 (with at least two vehicle control functions), and level 3 
(with control of all aspects of dynamic driving) connected and auto-
mated vehicles still require human override as and when needed while 
drivers of level 4 (with control of all aspects of dynamic driving) con-
nected and automated vehicles do not require human interaction on 
selected facilities (USDOT, 2016; Olia et al., 2018). 
Drivers’ understanding of the application, limitations of the tech-
nology, reliance, and behavioral response when driving vehicles with 
ADAS or automated features could be different than when compared to 
driving vehicles without ADAS. Studies conducted in the past indicate 
that not all drivers understand the application or limitations of advanced 
features in the vehicles. McDonald et al. (2018) reported that 21% of the 
vehicle owners did not understand the limitations of BSW while 33% of 
the vehicle owners did not know that the sensors engaging emergency 
braking system (EBS) could be blocked. Likewise, 40% of the vehicle 
owners misunderstood the application of FCW and AEB, believing that 
FCW system would automatically apply brakes (Fleet Manager, 2019). 
Furthermore, it is possible that advanced features make drivers more 
reluctant, distracted, or reduce their reactiontime when driving (Con-
sumer Reports, 2019). McDonald et al. (2018) reported that 29% of 
respondents to a survey felt comfortable engaging in other activities 
when provided with ACC, 30% did not do shoulder checks when pro-
vided with BSW, and 25% did not look back over their shoulder when 
provided with rear cross traffic alert. Likewise, 65% of the respondents 
to a survey trusted LKA to work every time while only 72% of the survey 
respondents trusted the ACC (Consumer Reports, 2019). Thus, the lack 
of understanding of the application, reliance, and limitations of the 
technology brings forth the argument whether the ADAS leads to other 
unforeseen influence on driver behavior. 
Most tests on ADAS were performed at safer conditions and with 
better trained drivers than what normal drivers encounter daily (Con-
sumer Reports, 2019). A few vehicle owners also complained of some 
advanced features not working properly at nighttime and during rain 
(Consumer Reports, 2019). Overall, drivers’ behavior when driving 
vehicles with ADAS compared to vehicles without ADAS has been 
meagerly explored. It is important to research and evaluate how drivers’ 
behavior by age, gender, and ethnicity varies when driving on rural 
roads, urban roads, and freeways in different lighting and weather 
conditions. Therefore, the focus of this research is in evaluating drivers’ 
behavioral response by their age, gender, and ethnicity when driving 
vehicles with ADAS like LDW, BSW, and OSW compared to vehicles 
without ADAS on different types of roads, lighting conditions, and 
weather conditions. 
A few studies were conducted by providing drivers with a test vehicle 
to capture and evaluate driving behavior (Dunn et al., 2019) or by 
conducting surveys (Kim et al., 2019). While these studies provide in-
sights on driver understanding of advanced features, they are not 
comprehensive, limited to selected scenarios, and may involve a long 
and cumbersome process. Privacy may also be at a trade-off. On the 
other hand, a driver simulator enables researchers with capturing a wide 
range of driver behaviors in response to customized driving scenarios 
and in a shorter span of time. Hence, a driving simulator was used to 
develop rural, urban and freeway driving scenarios with varying lighting 
and weather conditions and tested on male and female drivers (partic-
ipants) in various age groups. Some drivers were provided a vehicle with 
ADAS, while other drivers were provided a vehicle without ADAS. 
2. Literature review 
There are many studies documented in the literature on drivers’ 
behavior when driving on roads using low or high fidelity driver simu-
lators or through field observations. Examples include the effect of 
talking and texting while driving on drivers’ behavior (Strayer and 
Johnston, 2001; Strayer et al., 2003; Strayer and Drew, 2004; Strayer 
et al., 2006; Drews et al., 2009; Choudhary and Velaga, 2017; 
Choudhary and Velaga, 2019), drivers’ behavioral response to warning 
messages, congestion assistant, and variable speed limit (van Driel et al., 
2007; Lee and Abdel-Aty, 2008), effect on driver merging behavior 
based on the traffic condition (Calvi and De Blasiis, 2011), effect of voice 
recognition system on driver distraction accounting for age and envi-
ronmental conditions (Son et al., 2011), and visibility of two-wheelers 
encountered by left-turning motorists at urban intersections under 
nighttime condition compared to other hazards (Spivey and Pulugurtha, 
2016). Most of these studies evaluated effects on reaction time, visual 
attention, or crash risk by age group or gender. Kaptein et al. (1996) 
revealed that driver simulator-based study results are valid, and the 
validity increases with the resolution of the simulation and the presence 
of a moving base. Likewise, Espié et al. (2005) discussed influencing 
driver behavior and providing training-based certification using driver 
simulators. Contrarily, the transferability of skills from the driver 
simulator to the real-world was questioned by de Winter et al. (2012). 
There are also studies documented in the literature on drivers’ 
behavior when driving vehicles with advanced features. Hoogendoorn 
and Minderhoud (2002) observed improvement in capacity but reduc-
tion in reliability at bottlenecks when cruise control was deployed while 
no improvement in either capacity or reliability was observed when 
intelligent speed adaptation was deployed. 
Lundgren and Tapani (2006), Martin and Elefteriadou (2010), Maag 
et al. (2012), Rommerskirchen et al. (2014), and Witt et al. (2018) 
observed changes in driver behavior that could influence safety when 
vehicles equipped with ADAS or automated features are used in a driver 
simulator. Better lane keeping and reaction times may be achieved by 
engaging features like LDW and LKA despite varied driver behavior (Mas 
et al., 2011; Saleh et al., 2013). It is important that the driving envi-
ronment could be limited to cautious driving scenarios and avoid risky 
situations irrespective of any secondary tasks performed by the drivers 
(Aziz et al., 2013). Features like FCW and LDW with beeping sounds 
could distract the drivers and disrupt the vehicle trajectory as drivers 
may deviate from the lane, in contrast to their original anticipated 
functionality (Biondi et al., 2014; Gaspar et al., 2016). 
Studies conducted in the past focused primarily on the effectiveness 
of ADAS or their direct influence on operational performance or safety. 
For example, the effectiveness of an ACC system was measured in terms 
of maintaining headway (Pauwelussen and Feenstra, 2010). Similarly, 
LDW was evaluated to measure the lane departure behavior (Gaspar 
et al., 2016). Few researchers also investigated the user adaptability of 
ADAS, the effects of human–machine interaction, and probed into sug-
gesting their interface or interaction improvements (Hoogendoorn and 
Minderhoud, 2002; Rommerskirchen et al., 2014). The influence of 
FCW, LDW, LKA, and ACC (Pauwelussen and Feenstra, 2010; Aziz et al., 
2013; Saleh et al., 2013; Gaspar et al., 2016) were evaluated but only as 
a direct influence. However, there is an unforeseen influence on the 
overall driving behavior when provided with any type of ADAS which 
has been seldom ventured. The behavior could vary with the driving 
scenario and driver characteristics in addition to the ADAS. This 
manuscript explores to bridge some of these gaps by researching drivers’ 
behavioral response when driving vehicles with or without ADAS in 
rural, urban, and freeway driving scenarios in different lighting and 
R. Gouribhatla and S.S. Pulugurtha 
Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
3
weather conditions. 
3. Methodology 
The tools available in the National Advanced Driving Simulator 
(NADS) miniSim™ (The University of Iowa, 2016) were used to develop 
driving scenarios, capture driver behavior, and evaluate the influence of 
ADAS on driver behavior. Fig. 1 summarizes the methodology adopted 
in this research. It is categorized into four stages. The first stage involved 
developing appropriate driving scenarios. In order to improve the 
applicability of the results, rural, urban and freeway driving scenarios 
were simulated as these are the typical settings encountered by a driver. 
The second stage involved careful selection of driver participants 
such that the sample population is an accurate representation of the 
general population. The participants sixteen years or older were selected 
for the research. Each participant drove a test scenario (for ~4 min) to 
get acclimatized with the driver simulator and its features. They were 
then provided with all the three driving scenarios in the same sequence 
while a vehicle withor without ADAS was provided to them at random. 
The third stage involved the data processing and analysis to derive 
meaningful results. The fourth stage involved the identification of 
changes in driver behavior and applying them to identify the behavioral 
differences among vehicles with or without ADAS. 
Fig. 2 summarizes the functional flowchart of the process involved in 
developing the simulated driving scenario. The first step involved 
developing road network which is handled in the Tile Mosaic Tool 
(TMT). The initial road network along with the terrain conditions are 
built in the TMT. After developing the road network, it was imported 
into Interactive Scenario Authoring Tool (ISAT). This tool enables the 
users to define various driving conditions like the weather condition 
(clear, rain, snow, or fog), lighting condition (day or night), traffic light, 
or for a specific type of vehicle with a desired traffic. The output file 
from ISAT was then imported into miniSim™ where the scenario that 
has been put together is simulated and tested for driving. The output 
files are generated in data acquisition (daq) format and imported into 
MS Excel. 
It should be noted that the modules in each individual tool were used 
to develop customized driving scenarios. For example, the road tiles are 
already provided which were combined to form the road network. The 
ability to add vehicles, simulate different lighting and weather condi-
tions, and to generate certain driving conditions to test the ADAS are 
inherently available in the modules but were put together per the 
research objectives. 
Fig. 3 shows the driver simulator setup. The setup of the driver 
simulator consists of four screens, seating, and a panel with a driving 
wheel, brake, and accelerator. The screens are setup such that the pe-
ripheral vision cone of the driver is captured. The wheel and seat vibrate 
during the simulation and the simulator generates sound of the vehicle 
to emulate real-world driving. 
Fig. 4 shows snapshots of rural, urban, and freeway driving scenarios 
built in the driver simulator. 
The rural driving scenario was setup starting with two-lane undi-
vided roads (one lane in each direction) and extending into four-lane 
undivided roads (two lanes in each direction) primarily to assess the 
influence of BSW on driver behavior. The scenario consists of only one 
traffic signal and one all-way stop controlled intersection. The remain-
ing route mostly represents county roads. The vehicles interacting in the 
scenario consist of passenger cars, pickup trucks, and large trucks. The 
speed limits are set at 55 mph. The simulation also consists of a gravel 
road for a small portion, which is intended to capture driver behavior 
(change in speed or braking). This driving scenario is developed to last 
for 7 to 8 min. 
The urban driving scenario was setup such that the drivers interact 
with all the elements that are usually encountered while driving in 
urban conditions. Elements like traffic signals, passenger cars, light and 
large trucks, school buses and motorcycles are included in this scenario 
along with pedestrians. The speed limits are set at 45 mph or 50 mph. 
This driving scenario is developed to last 7 to 8 min and consists of four- 
lane undivided roads (two lanes in each direction). 
The freeway driving scenario is developed to last for 6 to 7 min and 
consists of two interchanges that allow the drivers to transition from one 
freeway to another freeway. The freeways are designed to be four-lane 
divided roads (two lanes in each direction). The speed limits are set at 
65 mph for the first freeway and 70 mph for the second freeway. The 
vehicles interacting in this scenario comprise mostly of large trucks and 
passenger cars. The simulation is setup to force an interaction between 
the drivers and large trucks while merging onto another highway. 
The developed driving scenarios simulate clear weather condition 
and daytime condition until specifically designed to display other 
weather (rain) or light condition (nighttime). 
As the focus is to the research the influence of ADAS on the driver 
behavior, the simulations are generated with LDW, BSW, and OSW in 
vehicles as the warning systems. They are used individually or in com-
binations during the simulations and compared with those without any 
ADAS. The simulator allows the users to assign advanced features to 
scenarios rather than vehicles, which can be enabled using “expressions” 
that can be added to the scenarios before initiating a simulation. 
The LDW was setup so that the “expression” trigger prompts the 
driver simulator to display a text on the screen warning the driver of a 
possible lane departure during the simulation. The driver would correct 
his/her path based on the warning. The number of lane departures from 
the simulations were extracted to evaluate the effectiveness of the LDW. 
The lane departure data was combined with the ADAS provision data. 
The BSW was setup to show a warning light on the mirror when 
another vehicle is in the blind spot. This system was simulated by setting 
up a car-following session and activating the BSW at the same time to 
Fig. 1. Methodology. 
R. Gouribhatla and S.S. Pulugurtha 
Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
4
capture the driver reaction. The BSW could also influence the brake 
pedal force. 
The OSW was setup so that the “expression” trigger displays a text 
alerting the driver of over-speeding. It was set to go off at two levels, one 
“expression” trigger at +5 mph and a second “expression” trigger at +10 
mph than the posted speed limit. The maximum speed is considered as a 
direct measure of the influence of OSW. 
4. Participant selection, ADAS, and driver behavior parameters 
Permission was obtained from the Institution Review Board (IRB) to 
conduct the research. The target participant population was determined 
as drivers sixteen years or older in age with a valid drivers’ license. 
While the selection cannot be pre-controlled, identifying gaps in the data 
(demographic and socio-economic) at every stage of the data collection 
process and selecting the participants to accommodate for the missing 
data points is necessary. Therefore, the individual participant selection 
criteria included factors like the demographic and socio-economic 
characteristics of the participants. 
Once a participant was selected for the study, he/she was given a 
small survey sheet that captured the demographic information of the 
participant. No personal information of the participant was collected in 
the survey to maintain anonymity. At the same time, the participant was 
also informed that their participation is completely voluntary, and he/ 
she could choose to drop out of the study at any point. Similarly, the 
participant could opt to not answer a question in the survey if he/she 
was not comfortable answering it. 
The consent forms were provided to the parent/guardian, if the 
participant was under 18 years to gain their consent to allow 
participation. 
Forty-three participants were invited, and data was collected from 
November 2020 through April 2021. About 46.5% of the participants 
are within the age range of 16–25 years, about 35% of the participants 
are within the age range 25–45 years, about 11.5% of the participants 
are within the age range 46–55 years, and about 7% of the participants 
are within the age range of 56–65 years. The maximum age of the par-
ticipants was 65 years. The gender distribution is 62.7% male and 37.3% 
female for the participant group. Similarly, about 46.5% of the partici-
pants are Caucasians, 23.3% of the participants are African Americans, 
7% of the participants are Hispanics, and 23.3% of the participantsare 
Asians. 
The education level of the participants is predominantly Bachelor’s 
and Master’s with 32.6% of the participants holding Bachelor’s degree 
and 30.2% of the participants holding a master’s degree. Also, a quarter 
of the participants possess a high school degree while associate degree is 
held by about 10% of the participants. The number of participants with a 
doctorate degree is ~2.3%. About 30% of the participants reported an 
annual household income of $150,000 or more, while a little over one 
fifth of the participants reported an annual household income of less 
than $25,000. About 14% of the participants reported an annual 
household income of $25,000 to $49,000 while 16.7% of the partici-
pants reported an annual household income of $100,000 to $150,000. 
About 11% of the participants reported an annual household income of 
$75,000 to $99,000. Overall, the sample (participants) is diverse and 
compares well with the population and their contribution to the vehicle 
miles traveled in the study area. 
The type of vehicle with or without ADAS provided to a participant 
varied between the three scenarios. LDW was provided individually to 
20 participants, BSW was provided individually to 21 participants, and 
OSW was provided individually to 18 participants. LDW and BSW were 
provided in combination to 9 participants, while LDW and OSW were 
provided in combination to 19 participants. Likewise, BSW and OSW 
were provided in combination to 13 participants. Ten participants were 
provided with a combination of all three ADAS and 19 participants were 
not provided any kind of advanced features. 
In total, LDW was provided to 17 participants in the rural driving 
scenario, 20 participants in the urban driving scenario, and 21 partici-
pants in the freeway driving scenario. Similarly, BSW was provided to 16 
participants in the rural driving scenario, 16 participants in the urban 
driving scenario, and 21 participants in the freeway driving scenario. 
Likewise, OSW was provided to 21 participants in the rural driving 
scenario, 21 participants in the urban driving scenario, and 18 partici-
pants in the freeway driving scenario. Overall, the vehicles with or 
without ADAS features were assigned based on the type of vehicle used 
Fig. 2. Functional flowchart of driving simulation development. 
Fig. 3. Driver simulator setup. 
R. Gouribhatla and S.S. Pulugurtha 
Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
5
by the participant in real-life and distributed fairly to ensure that the 
results and outcomes are unbiased. 
As stated previously, each participant drove a vehicle with or 
without ADAS at random covering all the three driving scenarios (rural, 
urban, and freeway) with replicated real-world driving conditions in the 
same sequence. Multiple parameters indicating driving behavior were 
captured for each participant, separately for each driving scenario. They 
are hard braking, hard cornering, lane departures, average speed (mph), 
average headway (feet), maximum speed (mph), and brake force (lbs). 
The hard braking represents the total number of times a participant 
has applied sudden brakes during a simulation. Similarly, hard corner-
ing is the total number of times a participant made sudden turnings in 
the simulation. The number of times the participant deviated out of his/ 
her lane in the simulation is represented by lane departures. These pa-
rameters indicate aggressive or unsafe driving behavior. 
The average speed is the speed maintained by a participant 
throughout a simulation and is measured in miles per hour. Similarly, 
the maximum speed is the maximum speed reached during a simulation 
by a participant in miles per hour. The average headway is measured in 
feet and is the distance maintained by a participant from the leading 
vehicle. The brake force is the average force applied on the brake by a 
participant during a simulation and is measured in pounds. 
5. Descriptive analysis 
Descriptive statistics such as minimum, mean, maximum, and stan-
dard deviation of selected driver behaviors were computed using data 
collected for 129 scenarios from 43 participants. They are summarized 
and shown as Tables A1–A6 in Appendix A, for various driving behaviors 
in rural, urban, and freeway driving scenarios; with or without ADAS, by 
lighting and weather condition, and by age, gender and ethnicity. 
The mean values of hard cornering and lane departures are higher for 
the participant group who drove a vehicle without the LDW in rural and 
freeway driving scenarios. The mean value of hard braking is higher 
while the mean value of average headway is lower for the participant 
group who drove a vehicle without the LDW in the rural driving sce-
nario. Contrarily, the mean values of hard cornering and lane departures 
are lower while the mean value of average headway is higher for the 
participant group who drove a vehicle without LDW compared to those 
who drove a vehicle with LDW in the urban driving scenario. The 
average speed, maximum speed, and brake force are similar for the two 
participant groups in the rural and urban driving scenarios. Overall, an 
aggressive driving behavior by the participant group who drove a 
vehicle without the LDW was observed in the rural and freeway driving 
scenarios while a relatively non-aggressive driving behavior by the 
participant group who drove a vehicle without the LDW was observed in 
the urban driving scenario. 
The participant group who drove a vehicle with the BSW seem to 
Fig. 4. Screenshots of rural, urban, and freeway driving scenarios. 
R. Gouribhatla and S.S. Pulugurtha 
Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
6
maintain lower headways in rural and urban driving scenarios but 
higher headways in the freeway driving scenario. The mean values of 
hard braking and average speed are lower for the participant group who 
drove a vehicle with the BSW in the rural driving scenario but lower for 
the participant group who drove a vehicle without the BSW in the urban 
driving scenario. Overall, the BSW seems to influence the car-following 
behavior of the participant group who drove a vehicle with the BSW only 
in rural and urban driving scenarios. 
The participant group who drove a vehicle without the OSW 
exhibited aggressive speeding and braking behaviors whereas the 
participant group who drove a vehicle with the OSW seem to display 
more aggressive car-following, lane following and brake force in rural 
and urban driving scenarios. However, only marginally aggressive 
speeding behavior was observed in the freeway driving scenario. 
More aggressive driving behavior was observed during the daytime 
condition while risky driver behaviors like hard cornering and lane 
departures are higher during the nighttime condition in the rural driving 
scenario. Likewise, more aggressive driving behavior was observed in 
the nighttime condition though the participants tried to maintain larger 
headways in urban and freeway driving scenarios. 
Likewise, more aggressive driving behavior was observed in the clear 
weather condition while rainy weather condition tended to make par-
ticipants more careful in the rural driving scenario. However, partici-
pants exhibited more aggressive driving behavior in the clear weather 
condition but a higher brake force during the rainy weather condition in 
the urban driving scenario. The mean value of average headway is 
higher for the participant group who drove in the rainy weather 
condition. 
The mean values of hard brakingare higher for the participant group 
>25 years of age compared to the participant group ≤25 years of age in 
rural and urban driving scenarios. While the mean values of average 
speed and maximum speed are higher for the participant group >25 
years of age compared to the participant group ≤25 years of age in the 
rural driving scenario, the participant group >25 years of age was 
observed to maintain longer headways and speed more than the 
participant group ≤25 years of age in the freeway driving scenario. 
The mean values of the average headway are higher for female 
participants compared to male participants in rural and freeway driving 
scenarios, while the male participants seem to apply hard brakes and 
more pressure while braking in the urban driving scenario. They also 
drove at higher speeds compared to female participants in the urban 
driving scenario. The mean values of lane departures and brake force are 
also higher for male participants indicating more aggressive driving 
behavior in the freeway driving scenario. 
The mean values of hard cornering events and brake force are higher 
for African American participants compared to other participants in the 
rural driving scenario while the mean values of hard braking are higher 
for Hispanic participants compared to other participants in the urban 
driving scenario. The mean values of lane departures are higher for 
Hispanic participants in the rural driving scenario but higher for African 
American participants in the urban driving scenario. Additionally, the 
mean value of average headway is higher for Hispanic participants 
compared to other participants in rural and freeway driving scenarios. 
The mean values of average speed and maximum speed are higher for 
Asian participants compared to other participants in rural and urban 
driving scenarios. 
6. Statistical analysis 
The descriptive analysis was followed with the analysis of variance 
(ANOVA) test to evaluate significant differences in mean values of driver 
behaviors like hard braking, hard cornering, lane departure, average 
speed, average headway, maximum speed, and brake pedal force. The 
intent is to determine if an ADAS can have a significant influence on the 
driver behavior. 
A one-way ANOVA test was performed, using IBM® SPSS® (2021), 
on the dataset as the sample sets were unequal. The inequality in the 
data samples is due to the random assignment of the ADAS to the 
participants. 
The null and alternate hypothesis are defined for each driver 
behavior. As an example, the null hypothesis states that there is no 
significant difference between the mean values of lane departure be-
tween the participant group who drove a vehicle with the LDW and the 
participant group who drove a vehicle without the LDW in the rural 
driving scenario. The alternate hypothesis states that there is a signifi-
cance difference in the mean values of lane departure between the 
participant group who drove a vehicle with the LDW and the participant 
group who drove a vehicle without the LDW in the rural driving 
scenario. 
The Eta-squared was computed to evaluate the percentage of vari-
ance in the driver behavior parameter. The null hypothesis was rejected 
or not rejected at a 95% confidence level based on the F and p-value. A p- 
value <0.05 was used to indicate a statistically significant difference 
between the mean values of the participant groups for the driver 
behavior parameter. 
Only the ANOVA results of the driver behavior parameters that were 
significant are discussed in this manuscript. The F is greater than 4.0, p- 
value is less than or equal to 0.05, and the F is greater than the F-critical 
for the reported driver behaviors. The Eta-squared values are generally 
lower between groups (lower effect size) when compared to within 
groups. 
Table 1 summarizes the ANOVA test results for each driver behavior 
parameter that were significant in the rural driving scenario. It can be 
inferred that the LDW reduces the lane departures. Likewise, the 
maximum speeds driven are lower for the participant group who drove a 
vehicle with the OSW compared to the participant group who drove a 
vehicle without the OSW. However, the mean values of average speed 
between the two participants groups are the same. The results also 
indicate that the BSW influences the brake pedal force. The results were 
also evaluated when the participant group was provided with any two 
ADAS compared with the participant group provided with one or no 
ADAS. The provision of two ADAS had a significant influence on the 
braking behavior. However, it did not have any significant influence on 
other driving behaviors. 
Table 1 also summarizes the ANOVA test results for each driver 
behavior parameter that were significant in the urban driving scenario. 
As in the case of rural driving scenario, it can be inferred that the LDW 
reduces the lane departures. However, the presence of the LDW in a 
vehicle could increase the number of hard cornering events on urban 
roads. A significant decrease in the maximum speed was observed with 
the provision of the OSW. However, the OSW was not found to have a 
significant influence on any other driver behavior parameters. No driver 
behavior parameters were significantly influenced by the BSW in the 
urban driving scenario. 
Table 1 also summarizes the ANOVA test results for each driver 
behavior parameter that were significant in the freeway driving sce-
nario. Even in this driving scenario, it can be inferred that the LDW 
reduces the lane departures. No driving behavior parameters were 
influenced by the BSW in the freeway driving scenario. Also, the influ-
ence of OSW was not found to be significant on either the maximum 
speed or the average speed in the freeway driving scenario. Further, 
OSW was not found to significantly influence any of the other driver 
behavior parameters as well in the freeway driving scenario. 
Table 2 summarizes the ANOVA test results for each driver behavior 
parameter that were significant by the lighting condition and the driving 
scenario. It can be inferred that the time of driving influences the 
average headway and lane departures in the rural driving scenario. A 
higher headway was maintained by the participant group who drove in 
the nighttime condition in the urban driving scenario. The lane de-
partures as well as the brake pedal force applied by the participants is 
higher in the nighttime condition in the urban driving scenario. The 
results also indicate that the lighting condition influences the average 
R. Gouribhatla and S.S. Pulugurtha 
Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
7
headway and lane departures while driving on a freeway. 
Table 3 summarizes the ANOVA test results for each driver behavior 
parameter that were significant by the weather condition and the 
driving scenario. The results indicate that the weather condition in-
fluences the average headway maintained by the participant group in 
the rural driving scenario. Higher headways were maintained while 
increased brake force was applied by the participant group who drove in 
the rainy weather condition in the urban driving scenario. Likewise, the 
weather condition significantly influenced the average speed and the 
average headway maintained by the participant group while driving on 
a freeway. 
No statistically significant differences in driver behavior parameters 
were observed when evaluated based on age, gender, or ethnicity. 
7. Conclusions 
Three different ADAS features were explored to evaluate their in-
fluence on the driver behavior. These warning systems influence the 
driving behaviors that they were specifically intended for. For example, 
the LDW was effective in influencing the lane departures ofthe partic-
ipants in all the three driving scenarios (rural, urban, and freeway). Also, 
the OSW was effective in influencing the maximum speed and average 
speed in some cases. The BSW did not have a significant influence on any 
of the driver behaviors. 
The hard cornering, lane departure, and average headway had 
distinct mean values for the participant group who drove a vehicle with 
the LDW compared to the participant group who drove a vehicle without 
the LDW. The mean values of different driver behaviors varied based on 
Table 1 
ANOVA Results – ADAS and Driving Scenarios. 
Source of Variation Driving Behavior Parameter & ADAS SS η2 df MS F P-value F-critical 
Rural Driving Scenario 
Between Groups Lane Departures – LDW 205.23 0.10 1 205.23 4.57 0.04 4.08 
Within Groups 1,840.54 0.90 41 44.89 
Total 2,045.77 42 
Between Groups Brake Pedal Force – BSW 615.2 0.10 1 615.20 4.44 0.04 4.08 
Within Groups 5,678.58 0.90 41 138.50 
Total 6,293.78 42 
Between Groups Maximum Speed – OSW 692.88 0.19 1 692.88 9.83 <0.01 4.08 
Within Groups 2888.48 0.81 41 70.45 
Total 3,581.37 42 
Between Groups Average Speed – OSW 3,423.67 0.30 1 3,423.67 17.54 <0.01 4.08 
Within Groups 8,003.22 0.70 41 195.20 
Total 11,426.89 42 
Urban Driving Scenario 
Between Groups Lane Departures – LDW 134.32 0.11 1 134.32 5.23 0.03 4.08 
Within Groups 1,053.96 0.89 41 25.71 
Total 1,188.28 42 
Between Groups Hard Cornering – LDW 15.07 0.23 1 15.07 11.97 <0.01 4.08 
Within Groups 51.63 0.77 41 1.26 
Total 66.70 42 
Between Groups Maximum Speed – OSW 4,876,652.00 0.64 1 4,876,652.00 74.94 <0.01 4.07 
Within Groups 2,733,042.00 0.36 42 65,072.42 
Total 7,609,694.00 43 
Freeway Driving Scenario 
Between Groups Lane Departures – LDW 1,218.98 0.36 1 1,218.98 22.72 <0.01 4.08 
Within Groups 2,199.44 0.64 41 53.65 
Total 3,418.42 42 
Note: SS, η2, df, MS, and F are sum-of-squares, Eta-squared, degrees of freedom, mean squares, and F ratio. 
Table 2 
ANOVA Results – Lighting Condition and Driving Scenarios. 
Source of Variation Driving Behavior Parameter SS η2 df MS F P-value F-critical 
Rural Driving Scenario 
Between Groups Average Headway 180,865.00 0.13 1 180,865.00 5.31 0.03 4.09 
Within Groups 1,226,487.00 0.87 36 340,691.00 
Total 1,407,352.00 37 
Urban Driving Scenario 
Between Groups Average Headway 909,143.00 0.17 1 909,143.00 7.56 <0.01 4.09 
Within Groups 4,330,524.00 0.83 36 120,292.00 
Total 5,239,667.00 37 
Between Groups Brake Pedal Force 1,016.15 0.13 1 1,016.15 4.95 0.03 4.09 
Within Groups 6,569.83 0.87 32 205.31 
Total 7,585.97 33 
Freeway Driving Scenario 
Between Groups Average Speed 121.54 0.13 1 121.54 5.39 0.02 4.09 
Within Groups 835.09 0.87 37 22.57 
Total 956.63 38 
Between Groups Average Headway 515,155.00 0.30 1 515,155.00 15.50 <0.01 4.09 
Within Groups 1,226,503.00 0.70 37 33,148.70 
Total 1,741,658.00 38 
Note: SS, η2, df, MS, and F are sum-of-squares, Eta-squared, degrees of freedom, mean squares, and F ratio. 
R. Gouribhatla and S.S. Pulugurtha 
Transportation Research Interdisciplinary Perspectives 13 (2022) 100545
8
the ADAS feature and the type of driving scenario. For example, in the 
case of LDW, while the mean values of lane departure varied in the 
urban driving scenario, the mean values of brake pedal force varied in 
the freeway driving scenario. 
It can be concluded that the type of influence the ADAS features had 
on the driver participants varied with the scenario. This is similar to the 
findings of Aziz et al. (2013). The LDW had a significant influence on 
lane departures irrespective of the driving scenario, similar to the 
findings of Gaspar et al. (2016). However, the OSW had a significant 
influence on the speeding behavior in rural and urban settings only. As 
participants tend to drive at relatively higher speeds on freeways, there 
is a lower chance of observing differences when the two participant 
groups are compared. 
While the BSW did not significantly influence any of the driver 
behavior parameters in the three driving scenarios, there was a signifi-
cant influence on the brake pedal force in the rural driving scenario. The 
activation of the BSW when a vehicle is in the adjacent lane could trigger 
a reaction from the participant to adopt safe maneuvering, possibly 
leading to the observed change in the brake pedal force. 
Significant differences in the average headway and lane departures 
were observed irrespective of the driving scenario (rural, urban, and 
freeway) between the participants groups who drove in different light-
ing condition. Likewise, significant differences in the average headway 
was observed irrespective of the driving scenario (rural, urban, and 
freeway) between the participants groups who drove in different 
weather condition. Significant differences in brake pedal force was also 
observed in urban driving scenario between the participants groups who 
drove in different lighting and weather conditions. However, the 
average speed was significantly lower only on freeways for the partici-
pants group who drove in the rainy weather condition. 
Overall, the research findings provide insights on the potential in-
fluence of ADAS on driver behavior by driving scenario, lighting con-
dition, and weather condition. Data was collected for 129 scenarios from 
43 participants in spite of the pandemic situation. Collecting additional 
data and evaluating the effectiveness of automated features like LKA, 
ACC, and AEB merit further investigation. 
The outcomes from a research based on driver simulator depends on 
the fidelity and features of the driver simulator. Also, participants in a 
driver simulator study know that there is little risk or consequence (e.g., 
crashing) for driving errors or risky driving behaviors. There is a need to 
study how driving behavior in a driver simulator transfers to driving 
behavior in real life. The findings could also be validated through video- 
trajectory based evaluations as it may be challenging to consider all 
driving scenarios and conditions when comparing with field data. 
CRediT authorship contribution statement 
Raghuveer Gouribhatla: Methodology, Formal analysis, Investiga-
tion, Data curation, Writing – original draft. Srinivas S. Pulugurtha: 
Supervision, Project administration, Funding acquisition, Conceptuali-
zation, Methodology, Investigation, Writing – review & editing. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Acknowledgments 
This manuscript is prepared based on information collected for a 
research project funded by the United States Department of Trans-
portation - Office of the Assistant Secretary for Research and Technology 
(USDOT/OST-R) University Transportation Centers Program (Grant # 
69A3551747127). The authors sincerely thank the staff of University of 
Iowa for their support with the NADS driver simulator. They also thank 
the study participants. 
Disclaimer 
This manuscript is disseminated in the interest of information ex-
change. The views, opinions, findings, and conclusions reflected in this 
manuscript are the responsibility of the authors only and do not repre-
sent the official policy or position of the USDOT/OST-R, or any other 
State, or the University of North Carolina at Charlotte or other entity. 
The authors are responsible for the facts and the accuracy of the data 
presented herein. This manuscript does not constitute a standard, 
specification, orregulation. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.trip.2022.100545. 
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ANOVA Results – Weather Condition and Driving Scenarios. 
Source of Variation Driving Behavior Parameter SS η2 df MS F P-value F-critical 
Rural Driving Scenario 
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Urban Driving Scenario 
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Note: SS, η2, df, MS, and F are sum-of-squares, Eta-squared, degrees of freedom, mean squares, and F ratio. 
R. Gouribhatla and S.S. Pulugurtha 
https://doi.org/10.1016/j.trip.2022.100545
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	Drivers’ behavior when driving vehicles with or without advanced driver assistance systems: A driver simulator-based study
	1 Introduction
	2 Literature review
	3 Methodology
	4 Participant selection, ADAS, and driver behavior parameters
	5 Descriptive analysis
	6 Statistical analysis
	7 Conclusions
	CRediT authorship contribution statement
	Declaration of Competing Interest
	Acknowledgments
	Disclaimer
	Appendix A Supplementary data
	References