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Journal of Transportation Technologies, 2022, 12, 420-438
https://www.scirp.org/journal/jtts
ISSN Online: 2160-0481
ISSN Print: 2160-0473
DOI:
10.4236/jtts.2022.123026 Jul. 21, 2022 420 Journal of Transportation Technologies
Vehicles, Advanced Features, Driver Behavior,
and Safety: A Systematic Review of the Literature
Raghuveer Prasad Gouribhatla, Srinivas Subrahmanyam Pulugurtha
IDEAS Center, Civil & Environmental Engineering Department, The University of North Carolina at Charlotte, Charlotte, USA
Abstract
Driver errors contribute to more than 94% of traffic crashes. Automotive
companies are striving to enhance their vehicles to eliminate driver errors
and reduce the number of crashes. Various advanced features like lane de-
parture warning
(LDW), blind spot warning (BSW), over speed warning
(OSW), forward collision warning (FCW), lane keep assist (LKA), adaptive
cruise control (ACC), cooperative ACC (CACC), and automated emergency
braking (AEB) are designed to assist with, or in some cases t
ake over, certain
driving maneuvers. They can be broadly categorized into advanced driver as-
sistance system (ADAS) and automated features. Each of these advan
ced
features focuses on addressing a particular task of driving, thereby, aiding the
driver, influencing their behavior, and enhancing safety. Many vehicles with
these advanced features are penetrating into the market, yet the total reported
number of crashes has increased in recent years. This paper presents a syste-
matic review of these advanced features on driver behavior and safety. The re-
view is categorized into 1) survey and mathematical methods to assess driver
behavior, 2) field test methods to assess driver behavior, 3)
microsimulation
methods to assess driver behavior, 4) driving simulator meth
ods to assess
driver behavior, and 5) driver understanding and the effectiveness of ad-
vanced features. It is followed by conclusions, knowledge gaps, and need for
further research.
Keywords
Vehicle, Advanced Driver Assistance System, Automated, Driver,
Behavior,
Safety
1. Introduction
Traffic deaths are a major issue worldwide. They are the leading cause of deaths
How to cite this paper:
Gouribhatla, R.P.
a
nd Pulugurtha, S.S. (2022) Vehicles, Ad-
vanced Features, Driver Behavior, and
Safe-
ty: A Systematic Review
of the Literature
.
Journal of Transportation Technol
ogies
,
12,
420
-438.
https:
//doi.org/10.4236/jtts.2022.123026
Received:
May 31, 2022
Accepted:
July 18, 2022
Published:
July 21, 2022
Copyright © 20
22 by author(s) and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
R. P. Gouribhatla, S. S. Pulugurtha
DOI:
10.4236/jtts.2022.123026 421 Journal of Transportation Technologies
among people up to 54 years in age in the United States [1]. Newer vehicles are
added to the roads with every passing year further aggravating the traffic con-
gestion and safety problem. As an example, more than 17.6 million passenger
cars and trucks were sold in 2016 alone while more than 3.21 trillion vehicle
miles traveled were estimated in 2018 [2] [3].
It is estimated that 94% of traffic crashes occur due to driver error [4]. These
errors are broadly classified into recognition errors, decision errors, performance
errors, and non-performance errors, and contribute to 41%, 34%, 10%, and 7%
of the crashes, respectively [5]. In general, non-performance errors are random
in nature and account for a relatively small percentage of driver errors but diffi-
cult to address.
As driver errors are the major contributor of traffic crashes, a continuous ef-
fort is being made by automotive companies and researchers to manufacture ve-
hicles with advanced features and reduce human intervention in driving, influ-
ence driver behavior as well as enhance safety, with the ultimate goal of complete
automation in the future. Figure 1 shows a schematic of example advanced fea-
tures.
The external advanced features are driven by sensors with varying detection
ranges. The adaptive cruise control (ACC) has the longest detection range and
uses long range radar systems while emergency braking and collision avoidance
systems use light detection and ranging (LiDAR). The warning or alerting systems
like blind spot warning (BSW) use sensors that have smaller detection ranges
Figure 1. Advanced features in a vehicle.
R. P. Gouribhatla, S. S. Pulugurtha
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10.4236/jtts.2022.123026 422 Journal of Transportation Technologies
while partially automated systems use sensors with longer detection ranges. These
systems also deliver progressive levels of assistance based on the user needs, as
classified by Safelite Auto Glass [6].
The advanced features can be broadly categorized into advanced driver assis-
tance system (ADAS) and automated features. BSW, lane departure warning
(LDW), over speed warning (OSW), and forward collision warning (FCW) are
example ADAS features. Likewise, ACC, cooperative ACC (CACC), lane keep
assist (LKA), and automated emergency braking (AEB) are example automated
features. ACC and LKA (also referred to as active lane keeping) are automated
features seen in many Level 1 and Level 2 connected and automated vehicles.
ACC is an automated system that maintains a designated speed and following
distance from the leading vehicle. This system can adjust its speed based on the
leading vehicle and can also make a complete stop if required. LKA is another
automated feature that ensures the vehicle stays in its lane by steering control of
the vehicle.
The penetration of vehicles with advanced features like ACC and CACC can
aid in better traffic flow performance, improve traffic stability, and influence
road capacity [7]-[14]. However, the effectiveness depends on the percent of ve-
hicles with such advanced features in the traffic stream [8] [13].
Agencies like the National Highway Traffic Safety Administration (NHTSA)
and the Federal Highway Administration (FHWA) have been, therefore, invest-
ing efforts to constantly monitor the performance of various emerging advanced
features and to evaluate their acceptance and ease of use via testing procedures
as well as penetration into the market in the United States [15]. Further, the
NHTSA [16] publishes articles and publicizes the advantages of ADAS, while
explaining their working mechanisms and limitations to help educate drivers.
Reviewing and investigating past research efforts invested into addressing is-
sues related with ADAS and automated features is vital to understand their ef-
fects on driver behavior and safety. Also, at the same time, this exercise helps in
identifying methodologies adopted by past researchers and any prevailing know-
ledge gaps, which serve as a guiding platform to establish a more concrete frame-
work going forward. An extensive synthesis of past literature was, therefore, car-
ried out. The remainder of this manuscript presents an overview of the past re-
search efforts categorized into 1) survey and mathematical methods to assess
driver behavior, 2) field test methods to assess driver behavior, 3) microsimula-
tion methods to assess driver behavior, 4) driving simulator methods to assess
driver behavior, and 5) driver understanding and the effectiveness of advanced
features.
2. Survey and Mathematical Methods to Assess Driver
Behavior
Abdul
et al
. [17] investigated driver behavior based on the pressure applied on
brake and gas pedals. They employed a cerebellum model articulation controller
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(CMAC) to model driver behavior. They observed the application of CMAC to
be reasonable for predicting various driver behavior characteristics and under-
stand the effects of a drivers’ emotion and his/her subconscious mind. Wang
et
al
. [18] evaluated driver behavior based on the acceleration and brake force pa-
rameters and steering wheel angle using mathematical models. They used these
parameters to incorporate into ADAS and observed that driver behavior varies
for different driving actions and generalizing driver behavior based on only a
few actions is not ideal. Similarly, Kamaruddin and Wahab [19] tried predicting
driver behavior based on speech configuration. They evaluated driver behavior
based on the emotion conveyed in their speech patterns and observed that it can
be used to profile driver behavior, especially when they are sleepy.
Kuge
et al
. [20] evaluated driver behavior using hidden Markov model (HMM).
They demonstrated the efficient application of HMM in both application and in
modeling driver behavior, particularly for lane change behaviors. Sathyanaraya-
na
et al
. [21] also developed an HMM framework to identify driver behaviors
and distractions using mathematical models. Tran
et al
. [22] used vision-based
foot gestures and HMM to analyze and predict braking behaviors of drivers.
While they used visual methods to capture driver behavior data, they employed
HMM to predict the pedal pressing gestures, and achieved a 94% accuracy by
this method.
Yannis
et al
. [23] investigated the acceptance of ADAS among older driver via
surveys from 23 European countries. Their results showed relatively better ac-
ceptance of ADAS among older drivers and females. Morignot
et al
. [24] eva-
luated the effectiveness of and acceptance of ADAS via a surveying method and
made recommendations for improving the technology in the future. Findings
from the past also indicate that the ratings of trust in ADAS technologies in-
creased with the length of vehicle ownership while unexpected system behavior
decreased participants ratings of trust over time [25].
3. Field Test Methods to Assess Driver Behavior
Alkim
et al
. [26] investigated the effects of LDW and ACC on driver behavior
using a field vehicle in Netherlands. They observed an 8% improvement in traf-
fic safety and a 3% reduction in fuel consumption. Additionally, they estimated a
10% reduction in emissions.
McCall
et al
. [27] focused on developing human-centric ADAS, such as pre-
dictive braking and ACC, and their effects on driver behavior using a test vehicle
in real-world driving conditions. Cognition-based adjustments were made to the
vehicle to capture driver behavior and the framework showed promising results.
Ziefle
et al
. [28] evaluated the effects of visual and auditory ADAS on older driv-
ers. They observed better driving performance in the absence of any ADAS,
while auditory systems contributed the highest to distraction. Their findings in-
dicate that older drivers preferred auditory systems over visual systems.
Inata
et al
. [29] modeled driver behavior using micro-electric sensors mounted
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on vehicles which were driven in real-world traffic environments. The sensing
equipment recorded the pedal operation of the vehicle, which was used for ana-
lyses. They developed a theoretical model to estimate driver behavior and then
compared it to the collected urban driving data to distinguish hurried driving
from relaxed driving. Angkititrakul
et al
. [30] used mathematical models (Gaus-
sian mixture model) and algorithms (piecewise auto regressive exogenous) to
understand driver behavior and incorporate them into car-following models.
The data used was obtained from real-world driving conditions. They captured
braking and acceleration parameters in response to the distance from leading
vehicle. The framework was then used to evaluate and model driver behavior.
Kondyli and Elefteriadou [31] investigated driver behavior using data obtained
from driver responses to various questions that addressed their thinking while
merging from a ramp onto a highway. They tried to correlate the driver’s beha-
vioral thinking to driver characteristics. Pauwelussen and Feenstra [32] investi-
gated the effects of LDW and ACC on driver behavior in real-world driving con-
ditions. They observed that ACC feature led to larger headways between vehicles
while manual override of the system resulted in shorter headways.
Farah and Koutsopoulos [33] probed into the effect of infrastructure to ve-
hicle (I2V) assistance systems on drivers using test vehicles. They observed re-
duced ranges of acceleration and deceleration while the car-following was more
synchronized. Olaverri-Monreal
et al
. [34] probed into the effect of the location
and angle of in-vehicle displays on driver safety. They observed the driver gaze
when looking at driver information systems in the vehicle that are currently ex-
isting in the market, and inferred that they meet the NHTSA guidelines for the
gazing away from road values. The driver preferences with the in-vehicle display
and location converged with that in the market, while mobile applications and
social media were not found to be necessary in the vehicle.
Son
et al
. [35] employed a road-testing method to evaluate the acceptance of
FCW and LDW based on the age and gender of the driver. While females and
younger drivers showed lowest acceptance of ADAS features, males and mid-
dle-aged drivers showed higher likelihood of acceptance. Miyajima
et al
. [36]
developed machine learning models to analyze data collected from real-world
driving conditions over 15 years. They observed various driver behaviors in-
cluding lane changes, car-following, and pedal operation. They developed statis-
tical models to predict risky driving and frustrated driving behaviors. Sieber
et
al
. [37] investigated driver behaviors in collision avoidance using a field test
study. They observed driver behavior and perception with different times of col-
lision, and observed that the movement speed of the obstacle had the greatest
effect on driver behavior.
Cades
et al
. [38] investigated the effects of LDW on driver behavior while the
participants performed a secondary task. They observed no significant effect of
LDW in reducing driver workload and driver cognition while performing sec-
ondary tasks. Lyu
et al
. [39] investigated the effect of ADAS on driver behavior
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using field operational tests in China on a test route. The effects of FCW and
LDW were primarily assessed in their study. They observed increased braking
time and decreased relative speed when provided with ADAS. Also, higher ac-
ceptance of FCW was observed over LDW. The acceptance was higher on free-
ways compared to urban roads.
4. Microsimulation Methods to Assess Driver Behavior
Kikuchi
et al
. [40] probed into the effect of using ACC in platooning, based on
the different positions of the vehicle, using microsimulation. They observed re-
duced reactions times to achieve stability in the platoon. Both, ACC equipped
and non-ACC equipped vehicles were observed to display enhanced safety. Der-
bel
et al
. [41] investigated the effect of mixed traffic, comprising of vehicles equipped
with ACC, in a crash scenario. Enhanced safety and reduced crash risk were ob-
served when vehicles equipped with ACC were involved in a crash.
Jeong
et al
. [42] investigated the effect of an inter-vehicle safety warning in-
formation system (ISWS), which communicates hazardous maneuvers of ve-
hicles that could lead to a crash. The driver behaviors captured using probe ve-
hicles were fed into VISSIM simulation, while the Surrogate Safety Assessment
Model (SSAM) was used to assess safety. Rear-end conflicts were observed to
reduce with penetration rates, while congestion increased. The standard devia-
tion of speed was observed to decrease by 40%.
Researching the effectiveness of multiple integrated systems, Li
et al
. [43] eva-
luated the effect of integrating I2V with ACC and variable speed limit (VSL) in
different combinations on traffic safety. The time exposed time to collision (TET)
which indicates the total time spent by a vehicle in safety-critical situation and
time integrated time to collision (TIT) which is time remaining for a collision to
occur if two vehicles continue to maintain the same speed were used as surrogate
safety measures in their study. The effect of integrating technologies established
better results when compared to individual effects. Employing a similar metho-
dology, Li
et al
. [44] evaluated the effects of ACC on safety of freeways. En-
hanced safety was observed with the increase in penetration rates, while the com-
bination of ACC and VSL were observed to produce the best results. Li
et al
. [45]
also investigated the effect of CACC on rear-end crash risk on freeways. A sig-
nificant reduction in crash risk was observed with CACC while the TET and TIT
reduced by over 90%.
Li
et al
. [46] designed simulation experiments to evaluate safety effects of ad-
vanced features like FCW, AEB, ACC, and CACC. Their findings indicate that
FCW and ACC perform poorly in reducing multi-vehicle rear-end crashes while
the AEB performs better due to automatic perception and reaction as well as the
full brake if needed during small-scale inclement weather conditions. The CACC
has the best performance as wireless communication provides a larger sight dis-
tance and a shorter time delay.
Likewise, Cicchino [47] analyzed the effectiveness of FCW, AEB, and a com-
R. P. Gouribhatla, S. S. Pulugurtha
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bination of both in reducing rear-end crashes. FCW, AEB, and combination of
both reduced rear-end crashes by 27%, 43%, and 50%, respectively. The vehicles
themselves being struck in rear-end crashes reduced in case of vehicles with in-
dividual systems but increased when the vehicles were equipped with both the
systems. In an attempt to investigate the effects of integrating connected vehicle
technology with other systems, Yue
et al
. [48] probed into integrating connected
vehicles with different ADAS. About a 70% reduction in crashes was achieved
with the integration, while FCW reduced rear-end crash risk by 35% in foggy
conditions.
5. Driving Simulator Methods to Assess Driver Behavior
Kaptein
et al
. [49] revealed that driving simulator-based study results are valid,
and that the validity increases with the resolution of the simulation and the
presence of a moving base. Strayer and Johnston [50] investigated the effect of
conversing on cellular phones on driving, using a driving simulator. They ob-
served longer reaction times to traffic lights while conversing, irrespective of
hand-held or hands-free devices. Similarly, in another driving simulator-based
study by Strayer
et al
. [51], using hands free devices for conversation was ob-
served to increase reaction times when stopping at intersections, due to reduced
visual attention.
Choudhary and Velaga [52] investigated the effects of talking and texting on a
phone on driving behavior in a suddenly arising situation (pedestrian crossing)
using a driving simulator. The mean speeds were observed to reduce if the driv-
ers were on phone, while the probability of a crash increased by 3 to 4 times.
Strayer and Drews [53] observed that the effect of cell phone conversations was
higher on young drivers compared to older drivers. In another study, Strayer
et
al
. [54] observed that the drivers were involved in a comparatively higher num-
ber of crashes when talking on cell phones owing to decreased reaction times to
braking, while intoxicated driving led to smaller headways from leading vehicles.
Overall, the effect of conversing and intoxication were observed to have similar
effects when the driving conditions and time to task were the same in their
study. Further, text messaging was also observed to constrain driver attention to
braking lights significantly leading to crashes [55].
Lundgren and Tapani [56] investigated the safety effects of ADAS using a driv-
ing simulator. They observed that the functionalities of ADAS and changes in
driver behavior for ADAS equipped vehicles could affect safety. Driver-vehicle
behavior was observed to substantially affect safety. van Driel
et al
. [57] evaluated
the effectiveness and acceptance of congestion assistant using a driving simulator.
They observed improved driver safety behaviors when approaching a traffic jam.
Lee and Abdel-Aty [58] captured driver responses to warning messages and VSL
using a driving simulator. They observed that the variation in driving speeds re-
duced, leading to better traffic flow and reduced congestion.
Hoogendoorn and Minderhoud [59] investigated the effects of intelligent cruise
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control and intelligent speed adaptation on driver behavior. They observed im-
proved capacities and reduced reliability at bottlenecks when cruise control was
deployed, while no improvement in either capacity or reliability was observed in
the case of intelligent speed adaptation. No improvement in safety was observed.
Martin and Elefteriadou [60] researched the effect of ADAS on driver behavior
using a driving simulator. They observed changes in driver behavior when using
vehicles equipped with ACC and lane change on arterials/ freeways. Calvi and
Blasis [61] evaluated the effect of driver behavior in acceleration lanes. They ob-
served that driver merging behavior was dictated by the traffic volume on main
roads and the acceleration lane length had no effect on their merging behavior.
Son
et al
. [62] assessed the effect of voice recognition system on driver distrac-
tion, especially for older drivers. The distraction effects were evaluated for both
urban and highway sections, and it was observed that both age and environ-
mental conditions effected driving behavior when the driver had to perform two
tasks. Mas
et al
. [63] investigated the effect of lateral control assistance systems
on driver behavior in avoiding obstacles using a driving simulator. They ob-
served an equal effect from both assisted and non-assisted drivers in avoiding
obstacles. However, the lateral control assistance system contributed to faster reac-
tion times.
Maag
et al
. [64] investigated the effects of ADAS on drivers, using single and
multi-driving simulators. They evaluated the effects of advanced features and
supported the use of multi-driving simulators in understanding and capturing
driver behavior. Saleh
et al
. [65] probed into the compatibility of driver and LKA
using a driving simulator. They observed better lane keeping when the system
was engaged, despite varied driver behavior. Aziz
et al
. [66] investigated the un-
derstanding of LDW and its effect on driver behavior using a driving simulator.
They observed that the dynamic nature of the driving environment could itself
limit the driving cognitive model leading to cautious driving scenarios that could
result in a tragedy, irrespective of any secondary tasks performed by the drivers.
Rommerkirchen
et al
. [67] investigated human-machine interactions to un-
derstand the effect of ADAS on drivers using a driving simulator. They observed
that the game-time (interaction) reduced in complex driving situations. In a
similar study, Biondi
et al
. [68] investigated the effect of a beeping ADAS on
driver behavior using a driving simulator. They observed that the beeping
sounds disrupted the vehicle trajectory as the drivers deviated from the lane.
They observed such sounds to be distracting for the driver in contrast to their
original functionality.
Spivey and Pulugurtha [69], using a low fidelity driving simulator, evaluated
the visibility of two-wheelers encountered by left-turning motorists at urban in-
tersections under nighttime conditions and compared them with other hazards.
The observed response times to a two-wheeler were not different from the
response times to a passenger car with two headlights. However, the response
times were significantly shorter than the times to recognize no hazard or a
two-wheeler with no headlight. Differences were observed when response times
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were compared for daytime and nighttime conditions.
Gaspar
et al
. [70] evaluated driver behavior when provided with FCW and
LDW using a driving simulator. They compared the effects on both distracted
and undistracted drivers and observed that the driver behaviors fell into catego-
ries based on distraction. Significant variation in driver lane change behavior was
also observed in their research. Witt
et al
. [71] investigated the effect of driver
characteristic and personality on driver behavior using virtual and driving simu-
lations. They attempted to develop driver cognitive model to help design ADAS.
Phone use was observed to significantly effect safe driving for both younger and
older drivers, with younger drivers having higher crash risk compared to expe-
rienced drivers in a driving simulator [72].
Gouribhatla and Pulugurtha [73] collected data for 129 scenarios and 43 par-
ticipants to evaluate the influence of LDW, BSW, and OSW on the driver beha-
vior. They observed that driver’s responses are different in rural, urban, and
freeway driving scenarios, and varied with their age, gender, ethnicity, lighting,
and weather condition. Automated systems like ACC and LKA were observed
to reduce the variation in driving behavior across different drivers compared to
both warning systems and no ADAS conditions [74]. Safer vehicle handling,
lane-following, and car-following behaviors were observed for drivers provided
with automated systems compared to drivers provided with warning systems
and drivers not provided with any ADAS.
6. Driver Understanding and the Effectiveness of Advanced
Features
Extensive efforts have been and are being expended to improve operational per-
formance and traffic safety by developing, testing, and implementing new ADAS
and automated features. Despite these efforts, a 14% increase in road related
deaths was recorded from 2014 to 2016 [75]. There have also been debates over
advanced features making drivers more reluctant and distracted, resulting in
unwanted side effects [75].
Eichelberger and McCartt [76], based on interviews of owners of 2010-2012
vehicles with ADAS and related features, observed that most respondents always
leave the features on, although fewer do so for LDW (59%). The ACC seem to be
aiding the drivers by following less closely while LDW seem to be aiding the
drivers in using turn signals more often. About one third of the respondents ex-
perienced autonomous braking when they believed they were at risk of crashing
while about one fifth of the respondents thought it had prevented a crash. How-
ever, about one fifth of the respondents were confused or misunderstood which
safety system had activated in their vehicle.
In a relatively recent study, McDonald
et al
. [77] revealed that 70% of drivers
preferred ADAS for their vehicles. While the preference and use of advanced
features seem to increase, the question of whether drivers understand these
technologies as expected still remains.
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A survey by the American Automobile Association (AAA) revealed that 21%
of vehicle owners assisted with BSW did not understand the limitations of the
system while Fleet Manager expected the number to be about 80% [77] [78]. On
the other hand, 33% of the vehicle owners did not understand that the sensors
engaging the Emergency Braking System (EBS) could be blocked [77]. Also, 40%
of drivers misunderstood the application of FCW believing that such a system
would automatically apply brakes [78]. While the extent of driver understanding
of ADAS is evident, what magnifies the issue of driver safety is their reliance on
such systems. It was reported that 29% of the respondents to a survey felt com-
fortable engaging in other activities when provided with ACC, 30% did not do
shoulder checks when provided with BSW, and 25% did not look back over their
shoulder when provided with rear cross traffic alert [77].
There are anticipated advantages of the advanced features. The LKA and LDW
were expected to mitigate over half a million crashes in 2016 alone [79]. The LKA
uses sensors at regular intervals to determine if the vehicle unintentionally moves
out of its travel lane and corrects the steering or other related aspects to main-
tain the vehicle in its travel lane [80]. It is expected to have significant effects on
safety, especially on run-off and head-on crashes [81] [82]. It is estimated that a
100% effective lane departure prevention system could reduce single vehicle run
off crashes by 65% [83]. While advantages are anticipated, tests and data also in-
dicate limitations of the advanced features.
ACC and LKA were tested under multiple driving conditions by the Insurance
Institute for Highway Safety (IIHS) in a series of track tests [84]. These tests re-
vealed that ACC reacted aggressively in some scenarios while failing to react to
already stopped vehicles [84]. Similarly, LKA was also observed to steer over the
shoulder in some cases where the lanes were not detected [84]. Drivers under-
standing such implications and taking control of the vehicle when needed is,
therefore, very important and influences their satisfaction as well as acceptance
of the advanced features.
A survey conducted by Consumer Reports [85] revealed that 74% of the res-
pondents were very satisfied with LKA while 85% of the respondents were very
satisfied with ACC. While 65% of the respondents trusted LKA to work every
time, ACC was trusted by 72% of the respondents [86]. Most tests related to
ACC and LKA in the past were performed under safer conditions compared to
real-world traffic conditions and with better trained drivers [86]. Also, it is possi-
ble that such systems make drivers more reluctant and less prompt when driving
[86]. Further, a few consumers also complained of LKA not working properly at
nighttime and during rain [86].
While it is difficult to precisely capture driver behavior in the real-world,
there have been few research studies where drivers were provided with a test ve-
hicle to capture and analyze driving behavior or by conducting surveys [87] [88].
Though these research studies captured some aspects of the driver understand-
ing, they are limited to selected scenarios and may involve a long and cumber-
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some process. Privacy may also be a trade-off. Thus, it is imperative that auto-
motive companies and researchers account for such aspects and conduct these
tests in a diverse range of real-world conditions to assess where and when the
advanced features might not yield expected outcomes.
Consumer Reports [89] considers ACC to be more of a luxury feature than a
safety feature owing to its functionality. Providing ACC along with other ADAS
may mask the minimal effectiveness of the system. Further, the efficient func-
tioning of ACC seems to vary based on the automotive companies offering it
[90]. ACC has been observed to be jerky with acceleration and braking maneuv-
ers, and its response to already stopped vehicles was discussed to be one of its
limitations. Additionally, it was observed that drivers with ACC were driving at
higher speeds compared to drivers without ACC [91].
The ACC and LKA features in combination control both the longitudinal and
lateral movements of a vehicle and provide a basis for a more advanced automated
driving version. The reliability of drivers on these systems also plays a vital role in
their effectiveness, as it dictates the attention they are paying while driving. Many
studies have highlighted the direct impacts of these features. But a deeper under-
standing of their effects on driving behavior needs to be investigated.
7. Conclusions, Knowledge Gaps, and Need for Future
Research
Extensive research was conducted on the effectiveness of advanced features in
influencing driver behavior. Various methodologies have been adopted to inves-
tigate the effects of advanced features in a vehicle on driver behavior. Metho-
dologies employing surveys and mathematical models are generally aimed to re-
search the adaptability of the methods in modeling driver behavior, although a
few researchers focused on studying the acceptance levels of different advanced
features. A few researchers also focused on predicting driver behavior, which
yielded reasonable results. However, these methods often rely on self-reporting
and the participants could be biased when answering the questions, especially
when they are being scrutinized by another person.
Field test methods were explored to capture driver behavior in some cases. A
few researchers looked at the acceptance rates of different advanced features
based on age and gender, while a few other researchers focused on the effect of
advanced features on driving behavior. Similarly, driving simulator studies have
been conducted to examine the effect of advanced features in certain conditions.
Most of the driving simulator studies did not take demographic characteristics
into consideration, nor did they compare participants from two demographic
groups (for example, young and old). The less than anticipated levels of accep-
tance of the advanced features and safety implications raises concern and em-
phasizes the need for a comprehensive, thorough, independent, and unbiased
research considering make, model, year of manufacturing, and the type of tech-
nology and functionality of each advanced feature.
R. P. Gouribhatla, S. S. Pulugurtha
DOI:
10.4236/jtts.2022.123026 431 Journal of Transportation Technologies
A persisting gap was observed in the previous studies, which tend to be more
hypotheses-driven, leading to concentrated research with reduced applicability.
The other limitation of the past studies is the investigation of only one or two ad-
vanced features at a time. There is a need to capture driver behavior when using
vehicles with advanced features, individually and together, in various real-world
driving situations to derive meaningful conclusions and understanding affects in
a multitude of cases.
The percentage of drivers relying on advanced features, the limitations that
apply to various advanced features, and the lack of a thorough understanding of
their implications can lead to many unsafe driving conditions. While the ad-
vanced features make driving tasks easier and comfortable, they may also make
driving more difficult and sometimes result in unsafe situations. The advanced
features take up certain driving tasks making a driver’s job easier to some extent,
but the driver needs to be cautious at all times to take over driving when needed
or as soon as any of these systems fail to react or disengage. This brings forth the
argument whether the advanced features lead to other unforeseen effects on
drivers. Thus, there is a need to assess by evaluating the behavior of drivers using
vehicles with advanced features and comparing with drivers using vehicles with-
out advanced features in a diverse range of real-world driving conditions and
scenarios (urban compared to rural, nighttime compared to daytime, icy/snowy
compared to rainy compared to cloudy compared to normal weather, different
types of curves/grades compared to straight/level sections, etc.) to better under-
stand the driving patterns and safety implications.
Acknowledgments
This paper is prepared based on information collected for a research project
funded by the United States Department of Transportation—Office of the Assis-
tant Secretary for Research and Technology (USDOT/OST-R) University Trans-
portation Centers Program (Grant # 69A3551747127). The authors thank Sonu
Mathew, Ph.D. of the IDEAS Center for his help with information and format-
ting of this paper.
Disclaimer
This paper is disseminated in the interest of information exchange. The views,
opinions, findings, and conclusions reflected in this paper are the responsibility
of the authors only and do not represent the official policy or position of the
University of North Carolina at Charlotte or other entity. The authors are re-
sponsible for the facts and the accuracy of the data presented herein. This paper
does not constitute a standard, specification, or regulation.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this pa-
per.
R. P. Gouribhatla, S. S. Pulugurtha
DOI:
10.4236/jtts.2022.123026 432 Journal of Transportation Technologies
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