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A Survey on Autonomous Vehicles Interactions with Human and other Vehicles

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Autonomous vehicles (AVs) or self-driving cars have the potential to replace human-operated cars. AVs can sense the environment and even navigate some of the roads in conditions humans find challenging. This may quickly lead to people's overreliance on AVs and overconfidence that no failures will occur. Therefore, AVs can impact society positively and negatively. AVs are X-ware systems that consist of software, hardware, humans, and their interactions. Despite the large number of studies on AVs, there are still a large number of unsolved problems. One major challenge for AVs is communication with other vehicles on the road as well as pedestrians. Replacing some of the human-operated vehicles with AVs will require interactions between AVs and these other users of the transportation network. Most of the previous research efforts consider software failures, whereas few consider the role of humans in the current transition to a society in which self-driving cars predominate. This paper considers three points of view: I) the driver and passenger of the AV, II) pedestrians, and III) AV interaction with other users of the transportation network. We also discuss related studies on human behavior.
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Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
A Survey on Autonomous Vehicles Interactions with Human and other
Vehicles
Bentolhoda Jafarya, Elaheh Rabieib, Mihai A. Diaconeasab, Hassan Masoomib, Lance
Fiondellaa, and Ali Moslehb
a University of Massachusetts Dartmouth, Dartmouth, USA
b University of California Los Angeles, Los Angeles, USA
Abstract: Autonomous vehicles (AVs) or self-driving cars have the potential to replace human-
operated cars. AVs can sense the environment and even navigate some of the roads in conditions
humans find challenging. This may quickly lead to people’s overreliance on AVs and overconfidence
that no failures will occur. Therefore, AVs can impact society positively and negatively. AVs are X-
ware systems that consist of software, hardware, humans, and their interactions. Despite the large
number of studies on AVs, there are still a large number of unsolved problems. One major challenge
for AVs is communication with other vehicles on the road as well as pedestrians. Replacing some of
the human-operated vehicles with AVs will require interactions between AVs and these other users of
the transportation network. Most of the previous research efforts consider software failures, whereas
few consider the role of humans in the current transition to a society in which self-driving cars
predominate. This paper considers three points of view: I) the driver and passenger of the AV, II)
pedestrians, and III) AV interaction with other users of the transportation network. We also discuss
related studies on human behavior.
Keywords: driver-pedestrian interaction, human intention, behavior analysis.
1. INTRODUCTION
Technological advances, such as artificial intelligence, are being leveraged to build our future smart
cities with the intelligent infrastructure in which driverless vehicles will be the key feature of the
transportation network. Commercial cars are categorized into 5 levels [1], including: (i) Level 1 cars
which are entirely manual; (ii) Level 2 cars in which only single operations such as anti-lock braking,
brake assist, and electronic stability are automated; (iii) In level 3 cars, called combined function
automation, two or more functions are automated; (iv) Level 4 cars are those which do not require
attention of the driver at any time because they use automation to control all aspects of the driving task
for extended periods; (v) Finally, level 5 cars are driverless and completely automatic. Nowadays
autonomous vehicles (AVs) or self-driving cars (level 4 and 5 cars) are in the research spotlight in
academia and of great interest to giant companies such as Apple, Google, Tesla, Uber, and Volvo [2].
AVs can sense the environment and navigate the roads even in conditions that are challenging for
humans to manage.
There have been numerous successes, since the early attempts at autonomy [3] and several studies on
autonomous vehicles have been published. Since 2004, the Defense Advanced Research Projects
Agency (DARPA) has held three major challenges on robotic vehicles [4]. In 2007, the DARPA urban
challenge focused on the research and development of robot cars for urban environments, which had to
navigate moving traffic safely while obeying California traffic regulations. However, they excluded
pedestrians and bicyclists [5] in their research. Later, Nothdurft et al. [6] introduced “Leonia” in the
Stadtpilot project, an autonomous vehicle, which demonstrated the ability to drive autonomously in
real urban conditions. They discussed the legal issues of driving AVs such as the role of driver, safety,
and control concepts. Mark et al. [7] reviewed some of the main technologies and architecture of
autonomous vehicles, and brought some of the emerging challenges and opportunities into
consideration, including navigation system, software integration, and algorithmic integration. Bagloee
[8] reviewed the challenges and opportunities that autonomous vehicles might create and, discussed
the possible advantages and disadvantages of the AVs. Bimbraw [9] reviewed the basic chronology of
autonomous vehicle technology. Tian et al. [10] proposed a tool for automated testing of a Deep-
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
Neural-Network-driven Autonomous Car capable of detecting behavior that could lead to crashes.
Panichella et al. [11] proposed a technique to detect the feature interaction failures in context of
autonomous vehicles by developing new search-based test generation algorithm.
Despite the large number of studies on AVs, the research on the interaction between human and AVs
is scarce yet indispensable [12]. Driving in an urban area is challenging because there are more
pedestrians in this area, which requires special considerations for AVs to be compatible in such an
uncertain environment. Moreover, AVs must interact with the other users of the road and human-
operated vehicles. Therefore, it is crucial to consider the challenges that driver and AV’s passengers,
pedestrians, and other users of the transportation network will likely face (Figure 1). This paper
reviews the relevant literature on these three areas with special focus on providing a better
understanding of the role of human interaction with AVs.
Figure 1: Interaction between AVs and pedestrian, AVs and driver, Vehicles to Vehicles (photo extracted from
[13])
The remainder of this paper is organized as follows: Section 2 reviews the literature on the impact of
AVs on pedestrians, communication between the vehicle to vehicle and vehicle to infrastructure, and
the role of the driver of the AVs. Section 3 provides conclusions and offers directions for future
research.
2. INTRACTION BETWEEN THE AVs AND PEDESRTAINS, VEHICLES, AND
DRIVER
This section reviews the literature on the AVs impact AVs have on pedestrians, interaction between
the AVs and the other road users, and role of the driver. Section 2.1 explains how the AVs may
negatively and positively effect pedestrians. Section 2.2 discusses the importance of vehicles to
vehicles and vehicles to infrastructure communication. Section 2.3 describes the role of the driver of
the AVs and how it may impact collisions.
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
2. 1 Interaction between AVs and pedestrians
Evidence reveals that autonomous vehicles are more cautious around pedestrians. Google’s
autonomous vehicles collision reports indicate that in most accidents the vehicles are hit from behind
because Google’s cars stop to give the right-of-way to the pedestrians [14]. Millard-Ball [15] analyzed
the interaction between the pedestrians and autonomous vehicles focusing on yielding at crosswalks
using game theory. Autonomous vehicles are programmed to respect the right-of-way of pedestrians,
which is conditional on AVs playing nice. Hulse et al. [16] surveyed almost 1,000 participants to
assess their perceptions on safety and acceptance of AVs. The results indicate that pedestrians believe
AVs are less risky compared to human-operated cars. Moreover, gender, age, and risk-taking
personality play an important role in AV acceptance. For example, females were less comfortable with
AVs than males and young adults.
In the case of level 5 of AVs, walking could become more pleasant because on-street parking is
anticipated to disappear, since driving will become a service and parking move to the suburbs.
Moreover, crossing the street should be more convenient, since the AVs must stop for pedestrians and
cannot claim that they did not see a pedestrian or drive under the influence of alcohol. Meeder et al.
[17] discussed the impact of AVs on pedestrian activity. They identified the potential positive impacts
of AVs on the pedestrians, i.e., AVs shows higher success rate in detecting the pedestrians compare to
human-operated cars. Therefore, walking could be safer and more attractive for pedestrians, since they
could cross the street with greater confidence. Furthermore, car pollution will decrease since most of
the AVs are expected to be electric. Therefore, the quality of air will improve, the noise level will
decrease, and the environment would become even more pleasant for pedestrians. Moreover, more
space is available for the pedestrians since the size of the AVs are smaller and they can drive within
narrower lanes. It is also anticipated that car sharing will be widespread.
Other researchers have mentioned the negative impacts of AVs on pedestrians. For example, Meeder
et al. [17] discussed potential abuse of AVs by pedestrians who could make them stop at every
location, which would increase congestion, and the pedestrians would have to take the longer paths as
they would likely be banned from not cutting through the AVs roadways at every location. More
importantly, communication between AVs and pedestrians is different, thus, pedestrians would need to
learn new rules, which they may resist. If AVs are more convenient, their use for short trips may be
preferred instead of walking, which will increase congestion and degrade the pedestrian experience.
Cities will be probably more organized, and it is unclear how attractive the city center will be to
different types of business. Furthermore, a driver’s license may no longer be needed and even children
could have their own private car. As a result, the number of autonomous cars may increase rapidly,
and walking areas may be dominated by AVs.
In contradiction to those who believe that AVs will be more cautious and accurate around pedestrians,
others believe that AVs are more likely to be the cause of a crash. Of course, the debate is ongoing. In
this regard, one of the central concerns is that AVs are not able to distinguish between different types
of objects they encounter with sufficient accuracy, which may threaten the life of pedestrians and lead
to incidents with serious consequences. Additionally, at this stage of automation and the current
conditions of the roads and traffic signs, AVs are susceptible to be adversely affected by pedestrians
such that some people such as a gang could simply stand in front of an AV or attack the car, in order
to steal it. In this case, security cameras on the cars with the ability to communicate with a police
station would be beneficial. Moreover, the physical design of an urban area needs to be remodeled, in
order to control the interaction between pedestrians and AVs to some extent, which may increase the
complexity of street design and create subsequent problems. In such case, individuals will have to
learn the new traffic signs and rules that requires time and impact transportation safety.
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
2.2 Interaction between AVs and other users of the transportation network
AV communication with the other parts of the transportation network such as human-driving cars and
bicycles is imperative, requiring further consideration. Radio signals are utilized for vehicle-to-vehicle
(V2V) and vehicle-to-infrastructure (V2I) communication. In V2V technology, the wireless
communication is employed by AVs to constantly share information such as their location, speed, and
intention to turn. Moreover, it allows AVs to sense their environment and maneuver properly to
prevent collisions [18]. Hobert et al. [19] explained and analyzed the different cases of V2V
communication and identified the requirements for safe and efficient operations. Aria et al. [20]
simulated different scenarios consisting of a 100% Automated vehicles (AV) and 100% conventional
vehicles (CV), investigating the impact of AVs on driver’s behavior and traffic performance. Their
results suggested the positive effect of AVs to reduce congestion, especially during the peak hours;
however, since driving AVs does not require too much attention, it may result in driver drowsiness and
inattentiveness in the long run due to its tedious nature.
Llatser et al. [21] evaluated Inter-Vehicle Communication (IVC) and analyzed the relation between
the frequency of the message passing (e.g., information about the neighboring vehicles and their
intention) and communication performance. They showed that the frequency of message passing has a
direct impact on the maneuvering performance i.e., high message passing frequency results in some
data loss, while low frequency leads to use of obsolete data. Utilizing V2V communication systems
plays a significant role in AV’s lane-keeping and obstacle-avoidance [22]. Zhu and Zhang [23]
proposed a simulation-based model to analyze the mixed traffic flow of human-driving and AVs. They
considered six different scenarios starting from the situation where all the vehicles are human-driving
to the case where all the vehicles are autonomous and concluded that there is a critical point on the
density-flux curve that distinguishes two opposite behaviors for mixed traffic flow before and after
that point.
Temel et al. [24] used orthogonal frequency division multiplexing (OFDM) techniques to analyze the
characteristics of the wireless channel prior to and during a crash in order to investigate the vehicle to
roadside barrier communication to enhance transportation safety. They presented real-world crash
result and emphasized the importance of the antenna height and directivity on the characteristics of
vehicle-to-barrier (V2B) channels. Sukuvaara and Nurmi [25] presented suggestions for the V2V and
V2I communication to improve traffic safety and smoothness by considering a wireless traffic service
platform based on simulations, pilot testing, and analysis.
To make AV a reality, synergy between AVs and other road users and understanding their intentions is
essential. It is critical that AVs being able to communicate not only with other AVs but also with the
human-driving cars. Obviously, the AVs of a particular manufacturer would be able to interact with
each other because they all use the same technology and program for communication. However, there
is a high risk of incompatibility between AVs produced by different manufacturers that should be
taken into consideration. For example, a Google car could cut off a Tesla car or not recognize the
intention of turn, which may result in a collision or other safety related incident. Consequently, the
traffic congestion would increase. Moreover, communication between the AVs and other road users is
based on the information received by AVs, which can be inaccurate, misinterpreted or even lost.
2.3 Interaction between AVs and driver
One of the critical deficiencies in AVs is the driver’s susceptibility to erroneous or delayed decision-
making following an alarm from the car, similar to those observed in the aviation industry. In order to
avoid undesirable consequences, this process of decision-making and taking the appropriate action
should usually take place in a very short time window after the alarm goes off. In addition, the
frequency of these incidents likely to be orders of magnitude greater than the aviation industry, given
the number of cars on the road relative to the number of planes in the sky. This process of recovering
after AV’s inability to continue to operate can be impaired by a myriad of factors such as the driver
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
being distracted (taking a nap, talking with other passengers, reading, etc.,) or being unconscious of
type of failure such as a malfunction in the speed control system.
In automated driving, the driver may be deeply engaged in other activities, thus bringing a distracted
driver back into the control loop can become very challenging. In fact, transitions between the human
and automated driving is a key design issue for autonomous vehicles. Merat et al. [26] employed a
driving simulator to investigate the ability of drivers to handle conditions where automation reverts to
manual control, which was based on the length of the time the driver was not looking at the road
ahead. They considered eye movement patterns and showed that drivers exhibited the best
performance when the control transferred after six seconds after a take-over request. Moreover, they
discussed the importance of designing effective human machine interfaces in automated driving
conditions, which certify the time and manner in which the message regarding transfer to the manual
control is issued. Another imperative factor is how to warn the driver, for example, the necessity of
clear language [27] to unambiguously communicate the level of urgency to the driver. Politis et al.
[28] considered a language-based warning model to switch from autonomous to manual control. They
evaluated the audio, tactile, and visual warnings and concluded that it is critical for the driver to
intuitively understand the level of urgency.
From a human factors perspective, the crucial challenges are designing automation in a way that
drivers fully understand the functionalities, capabilities and limitations of the vehicle, and how to keep
the driver engaged to maintain situational awareness of what the vehicle is doing and when manual
intervention is needed. Cunningham and Regan [29] reviewed some of the human factors challenges in
this regard including driver inattention and distraction, skill degradation, and motion sickness.
Petermeijer et al. [30] reviewed the literature on vibro-tactile displays as a possible method to alert the
driver at the time of transition from automated to manual driving. Four dimensions were considered,
including frequency, amplitude, location, and timing. Although vibrotactile feedback has benefits, it
also has several limitations such as differences in the response threshold of individuals to receive
notice and duration or intensity of vibration that may be uncomfortable. Lu et al. [31] proposed a
theoretical framework and investigated the human factors in transition from automated to manual
driving by defining different joint driving states of driver and vehicle. Kyriakidis et al. [32]
interviewed 12 expert researchers in the field of human factors and discussed the role of human factors
in AVs. They identified the commonalities and perspectives regarding human factors. It was
recommended that drivers be trained to be aware of AV limitation to ensure they are capable of
operating AVs and maintain control of the car in case of transition from autonomous to the manual
driving.
Clark et al. [33] analyzed the impact of level of distraction with respect to the age of drivers when
predicting the performance of taking control of a highly automated vehicle. They showed that younger
drivers were more easily distracted than older drivers. Moreover, age and speed were negatively
correlated with high speed among younger drivers. However, their study had some limitations, such
as small sample size and the type of activities that participants were engaged in to achieve different
level of distraction, which may have resulted in limitations to the generalizability of results. Vogelpohl
et al. [34] studied the behavior of distracted drivers as they reacted to the unexpected traffic events.
Their results indicated most participants reacted to the unexpected conditions and deactivated the
automation after seven to eight seconds. Moreover, drivers of the automated vehicles exhibited a
delay, up to five additional seconds before the first gaze into the mirror and road in comparison with
the drivers of the manual cars.
Another significant factor that needs to be considered is the driver's driving skills. It is critical that
driver be able to respond in case of automation failure. Lack of driving skills can be serious and may
threaten the life of pedestrians, drivers, and passengers of an AV, although some other factors such as
gender, age, level of consciousness are also important.
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
As discussed, operating an AV will allow the driver to be easily distracted. Therefore, the time to
recognize AV failure and resume manual control will increase. One solution is to use eye detection
technology that can track the driver’s eyes and alert the driver when the driver is not focused enough.
Since reaction time plays a critical role in case of automation failure, it would also be valuable if AVs
could predict when something might go wrong and alert in advance.
3. CONCLUSION
This paper considers three categories of AV interactions including: I) pedestrian, II) vehicle to
vehicle/infrastructure, and III) drivers and passengers. The recently published papers in this area were
reviewed and the gaps requiring additional focus were identified. Most studies assume AVs will play
nice. Although this assumption simplifies the experiments, AVs experience failures, which create
unforeseen problems. More studies regarding interaction with pedestrians are needed to develop
methodologies and algorithms so that AVs can make robust decisions on what action or sequences of
actions would mitigate consequences when confronted with challenging situations. Moreover, current
transportation network is not designed for AVs. Therefore, AV interactions with pedestrians have not
been considered in the process of their design. For vehicle-to-vehicle and vehicle-to-infrastructure
category, communication between AVs of different companies requires standards and protocols to be
compatible, so that they can share information and intentions with each other to reduce the risk of
collision. Moreover, interaction between AVs and human-driven cars need to be investigated for the
same purpose. In both cases, the reliability of the information being transferred between vehicles
needs to be assured, since inaccuracy in the data transferred could result in an incident. A final
category is the interaction between an AV and drivers and passengers of that AV. The driving skills
and possible loss of situation awareness of the driver need to be studied thoroughly to increase the
reliability of AVs. For example, a driver with low or degraded skills from lack practice, may perform
an incorrect action in a situation that could lead to a collision. Moreover, since driving an AV may be
a monotonous task, the driver may become easily distracted by other activities making them more
prone to taking inappropriate actions when human intervention is required.
Future work will expand this work and consider the impact of AVs on pedestrians, drivers,
infrastructure and other users of the road. More specifically, we will discuss the possible failures in
greater detail and will offer potential solution and methods to objectively measure efforts to make
improvements that enhance safety and convenience.
References
[1] S. Casner, E. Hutchins, and D. Norman. "The challenges of partially automated driving."
Communications of the Association for Computing Machinery, 59.5 (2016): 70-77.
[2] L. Hook, and R. Waters. "Google’s Waymo passes milestone in driverless car race." Financial
Times. https://www. ft. com/content/dc281ed2-c425-11e7-b2bb-322b2cb39656. Accessed 28 (2018).
[3] F. Kro ̈ger, Automated driving in its social, historical and cultural contexts,in Autonomous
Driving. New York, NY, USA: Springer-Verlag, 2016, pp. 4168.
[4] U. Ozguner, C. Stiller, and K. Redmill. "Systems for safety and autonomous behaviour in
cars: The DARPA Grand Challenge experience." Proceedings of the IEEE 95.2 (2007): 397-412.
[5] C. Reinholtz et. al. DARPA: Urban Challenge Technical Evaluation Criteria. Technical
report, DARPA, Arlington, VA, USA (2006)
[6] T. Nothdurft, P. Hecker, S. Ohl, F. Saust, M. Maurer, A. Reschka, and J. Ru ̈diger Bo ̈hmer.
"Stadtpilot: First fully autonomous test drives in urban traffic." International IEEE Conference on
Intelligent Transportation Systems, 2011.
[7] M. Campbell, M. Egerstedt, J. How, R. Murray. "Autonomous driving in urban environments:
approaches, lessons and challenges." Philosophical Transactions of the Royal Society of London A:
Mathematical, Physical and Engineering Sciences 368.1928 (2010): 4649-4672.
[8] S. Bagloee, M. Tavana, M. Asadi, and T. Oliver. "Autonomous vehicles: challenges,
opportunities, and future implications for transportation policies." Journal of Modern Transportation
24.4 (2016): 284-303.
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
[9] K. Bimbraw. "Autonomous cars: Past, present and future a review of the developments in the
last century, the present scenario and the expected future of autonomous vehicle technology."
International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vol. 1.
IEEE, 2015.
[10] Y. Tian, K. Pei, S. Jana, and B. Ray. "Deeptest: Automated testing of deep-neural-network-
driven autonomous cars." Proceedings of the International Conference on Software Engineering.
ACM, 2018.
[11] A. Panichella, S. Nejati, L. Briand, T. Stifter, et al. "Testing Autonomous Cars for Feature
Interaction Failures using Many-Objective Search." Proceedings of the 33rd IEEE/ACM International
Conference on Automated Software Engineering, 2018.
[12] I. Wolf. "The interaction between humans and autonomous agents." Autonomous driving.
Springer, Berlin, Heidelberg, 2016. 103-124.
[13] B. Grush, (2018). The Rise of Autonomous Vehicles: Planning for Deployment Not Just
Development. Retrieved from https://www.rdmag.com/article/2018/01/rise-autonomous-vehicles-
planning-deployment-not-just-development
[14] E. Aria, J. Olstam, and S. Christoph. "Investigation of automated vehicle effects on driver's
behavior and traffic performance." Transportation Research Procedia 15 (2016): 761-770.
[15] A. Millard-Ball. "Pedestrians, autonomous vehicles, and cities." Journal of planning education
and research 38.1 (2018): 6-12.
[16] L. Hulse, H. Xie, and E. Galea. "Perceptions of autonomous vehicles: Relationships with road
users, risk, gender and age." Safety Science 102 (2018): 1-13.
[17] M. Meeder, E. Bosina, and U. Weidmann. "Autonomous vehicles: Pedestrian heaven or pedes-
trian hell?" Swiss Transport Research Conference, 2017.
[18] W. Cunningham, “Honda tech warns drivers of pedestrian presence,” Online, 2017-06-30.
[Online]. Available: https://www.cnet.com/ roadshow/news/honda- tech- warns- drivers- of-
pedestrian- presence/
[19] L. Hobert, A. Festag, I. Llatser, L. Altomare, F. Visintainer, and A. Kovacs. "Enhancements
of V2X communication in support of cooperative autonomous driving." IEEE communications
magazine 53.12 (2015): 64-70.
[20] E. Aria, J. Olstam, and C. Schwietering. "Investigation of automated vehicle effects on driver's
behavior and traffic performance." Transportation Research Procedia 15 (2016): 761-770.
[21] I. Llatser, A. Festag, and G. Fettweis. "Vehicular communication performance in convoys of
automated vehicles.", IEEE International Conference on Communications, 2016.
[22] H. Fahmy, G. Baumann, M. El Ghany, and H. Mostafa. "V2V-based vehicle risk assessment
and control for lane-keeping and collision avoidance." International Conference on Microelectronics,
2017.
[23] W. Zhu, and H. M. Zhang. "Analysis of mixed traffic flow with human-driving and
autonomous cars based on car-following model." Physical A: Statistical Mechanics and its
Applications 496 (2018): 274-285.
[24] S. Temel, M. Vuran, M. Lunar, Z. Zhao, A. Salam, R. Faller, and C. Stolle. "Vehicle-to-
barrier communication during real-world vehicle crash tests." Computer Communications, 2018.
[25] T. Sukuvaara, and N. Pertti. "Wireless traffic service platform for combined vehicle-to-
vehicle and vehicle-to-infrastructure communications." IEEE Wireless Communications 16.6, 2009.
[26] N. Merat, A. Jamson, F. Lai, M. Daly, and O. Carsten. "Transition to manual: Driver
behaviour when resuming control from a highly automated vehicle." Transportation research part F:
traffic psychology and behaviour 27 (2014): 274-282.
[27] C. Baldwin, and M. Colleen. "Perceived urgency, alerting effectiveness and annoyance of
verbal collision avoidance system messages." Proceedings of the Human Factors and Ergonomics
Society Annual Meeting. Vol. 46. No. 22. Sage CA: Los Angeles, CA: SAGE Publications, 2002.
[28] I. Politis, S. Brewster, and F. Pollick. "Language-based multimodal displays for the handover
of control in autonomous cars." Proceedings of the International Conference on Automotive User
Interfaces and Interactive Vehicular Applications, 2015.
[29] M. Cunningham, M. A. Regan. "Autonomous vehicles: human factors issues and future
research." Proceedings of the Australasian Road Safety Conference. 2015.
Probabilistic Safety Assessment and Management PSAM 14, September 2018, Los Angeles, CA
[30] S. M. Petermeijer, J. CF De Winter, and K. J. Bengler. "Vibrotactile displays: A survey with a
view on highly automated driving." IEEE Transactions on Intelligent Transportation Systems 17.4
(2016): 897-907.
[31] Z. Lu, R. Happee, C. Cabrall, M. Kyriakidis, and J. de Winter. "Human factors of transitions
in automated driving: A general framework and literature survey." Transportation research part F:
traffic psychology and behaviour 43 (2016): 183-198.
[32] M. Kyriakidis, J. de Winter, N. Stanton, T. Bellet, B. van Arem, K. Brookhuis, M. Martens, K.
Bengler, J. Andersson, and N. Merat et al. "A human factors perspective on automated driving."
Theoretical Issues in Ergonomics Science (2017): 1-27.
[33] H. Clark, A. McLaughlin, B. Williams, and J. Feng. "Performance in takeover and
characteristics of non-driving related tasks during highly automated driving in younger and older
drivers." Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Vol. 61. No. 1.
Sage CA: Los Angeles, CA: SAGE Publications, 2017.
[34] T. Vogelpohl, M. Kühn, T. Hummel, T. Gehlert, and M. Vollrath. "Transitioning to manual
driving requires additional time after automation deactivation." Transportation research part F: traffic
psychology and behaviour 55 (2018): 464-482.
... However, their overview was not based on an SLR, and they did not consider some sensor/actuator categories, e.g., vestibular, olfactory, and gustatory. Besides, there are reviews regarding human factor-related issues (e.g., distraction, awareness, trust, or acceptance) [76,164] and technical challenges [157]. The first design space for driver-based automotive UIs was introduced by Kern and Schmidt [177] that describes in-vehicle input and output modalities concerning their location in the interior. ...
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Automotive user interfaces constantly change due to increasing automation, novel features, additional applications, and user demands. While in-vehicle interaction can utilize numerous promising modalities, no existing overview includes an extensive set of human sensors and actuators and interaction locations throughout the vehicle interior. We conducted a systematic literature review of 327 publications leading to a design space for in-vehicle interaction that outlines existing and lack of work regarding input and output modalities, locations, and multimodal interaction. To investigate user acceptance of possible modalities and locations inferred from existing work and gaps unveiled in our design space, we conducted an online study (N=48). The study revealed users' general acceptance of novel modalities (e.g., brain or thermal activity) and interaction with locations other than the front (e.g., seat or table). Our work helps practitioners evaluate key design decisions, exploit trends, and explore new areas in the domain of in-vehicle interaction.
... Road users are modeled with sequential actions and environmental factors such as the vehicle speed is taken into account for payoffs and the model was tested in simulation. Jafary et al. (2018) surveyed autonomous vehicles' interactions with other traffic participants, including pedestrians and concluded that more research is needed for advanced methodologies and algorithms for a more robust AV decisionmaking. Zhang et al. (2022) developed a Bayesian game-based decision model for road user interactions including a method to evaluate the opponent's aggressiveness, and a Turing test is performed to evaluate the human-likeness of the approach. ...
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... However, their overview was not based on an SLR, and they did not consider some sensor/actuator categories, e.g., vestibular, olfactory, and gustatory. Besides, there are reviews regarding human factor-related issues (e.g., distraction, awareness, trust, or acceptance) [76,164] and technical challenges [157]. The first design space for driver-based automotive UIs was introduced by Kern and Schmidt [177] that describes in-vehicle input and output modalities concerning their location in the interior. ...
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... Recent research examined more diverse interactions not only covering in-vehicle but also with-vehicle interactions [3]. Such research includes the study on factors that support the passenger's non-driving activities [8] or the study examining other users of the transportation network [13], including pedestrians or other cars. ...
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... Accidents on the roads mostly occur due to common human mistakes such as mobile phone or other distractions, driver drowsiness and tiredness [8]. Machines don't feel fatigue and don't get distracted by things like mobile phone while driving and hence these cars don't make careless mistakes while driving. ...
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Fully automated self-driving cars, with expected benefits including improved road safety, are closer to becoming a reality. Thus, attention has turned to gauging public perceptions of these autonomous vehicles. To date, surveys have focused on the public as potential passengers of autonomous cars, overlooking other road users who would interact with them. Comparisons with perceptions of other existing vehicles are also lacking. This study surveyed almost 1000 participants on their perceptions, particularly with regards to safety and acceptance of autonomous vehicles. Overall, results revealed that autonomous cars were perceived as a “somewhat low risk“ form of transport and, while concerns existed, there was little opposition to the prospect of their use on public roads. However, compared to human-operated cars, autonomous cars were perceived differently depending on the road user perspective: more risky when a passenger yet less risky when a pedestrian. Autonomous cars were also perceived as more risky than existing autonomous trains. Gender, age and risk-taking had varied relationships with the perceived risk of different vehicle types and general attitudes towards autonomous cars. For instance, males and younger adults displayed greater acceptance. Whilst their adoption of this autonomous technology would seem societally beneficial – due to these groups’ greater propensity for taking road user risks, behaviours linked with poorer road safety – other results suggested it might be premature to draw conclusions on risk-taking and user acceptance. Future studies should therefore continue to investigate people’s perceptions from multiple perspectives, taking into account various road user viewpoints and individual characteristics.
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We investigated the mixed traffic flow with human-driving and autonomous cars. A new mathematical model with adjustable sensitivity and smooth factor was proposed to describe the autonomous car’s moving behavior in which smooth factor is used to balance the front and back headway in a flow. A lemma and a theorem were proved to support the stability criteria in traffic flow. A series of simulations were carried out to analyze the mixed traffic flow. The fundamental diagrams were obtained from the numerical simulation results. The varying sensitivity and smooth factor of autonomous cars affect traffic flux, which exhibits opposite varying tendency with increasing parameters before and after the critical density. Moreover, the sensitivity of sensors and smooth factors play an important role in stabilizing the mixed traffic flow and suppressing the traffic jam.
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Automated vehicles are those in which at least some aspects of a safety-critical control function occur without direct driver input. It is predicted that automated vehicles, especially those capable of " driving themselves " , will improve road safety and provide a range of other transport and societal benefits. A fundamental issue, from a human factors perspective, is how to design automation so that drivers understand fully the capabilities and limitations of the vehicle, and maintain situational awareness of what the vehicle is doing and when manual intervention is needed – especially for first generation vehicles that require drivers to resume manual control of automated functions when the vehicle is incapable of controlling itself. The purpose of this paper is to document some of the human factors challenges associated with the transition from manually driven to self-driving vehicles, and to outline what we can be doing in Australia, through research and other means, to address them.