Pedestrian Behavior Interacting with Autonomous Vehicles: Role of AV Operation and Signal Indication and Roadway Infrastructure

Interacting with pedestrians is challenging for Autonomous vehicles (AVs). This study evaluates how AV operations /associated signaling and roadway infrastructure affect pedestrian behavior in virtual reality. AVs were designed with different operations and signal indications, including negotiating with no signal, negotiating with a yellow signal, and yellow/blue negotiating/no-yield indications. Results show that AV signal significantly impacts pedestrians' accepted gap, walking time, and waiting time. Pedestrians chose the largest open gap between cars with AV showing no signal, and had the slowest crossing speed with AV showing a yellow signal indication. Roadway infrastructure affects pedestrian walking time and waiting time

Studying Pedestrian’s Unmarked Midblock Crossing Behavior on a Multilane Road When Interacting With Autonomous Vehicles Using Virtual Reality

This dissertation focuses on the challenge of pedestrian interaction with autonomous vehicles (AVs) at unmarked midblock locations where the right-of-way is unspecified. A virtual reality (VR) simulation was developed to replicate an urban unmarked midblock environment where pedestrians cross a four-lane arterial roadway and interact with AVs. One research goal is to investigate the impact of roadway centerline features (undivided, two-way left-turn lane, and median) and AV operational schemes portrayed through on-vehicle signals (no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications) on pedestrian crossing behavior. Results demonstrate that both roadway centerline design features and AV operations and signaling show significant impacts on pedestrians\u27 unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. Whereas, only the roadway centerline design features significantly impact the walking time, and only the AV operations and signaling significantly impact the accepted gap. Participants in the undivided centerline scene spent longer time waiting at the curb and walking on the road. Also, pedestrians are more likely to display risky behavior and cross in front of AVs indicating blue signals with non-yielding behavior in the presence of a median centerline scene. The inclusion of a yellow signal, which indicates the detection of pedestrians and signifies that the AVs will negotiate with them, resulted in a significant reduction in pedestrian waiting time both at the curb and in the middle of the road, when compared to AVs without a signal. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle of the road. It is also found that older pedestrians tend to wait longer at the curb and are less likely to cross in front of AVs showing a blue signal with non-yielding behavior. Another research goal is to investigate how this VR experience change pedestrians’ perception of AVs. Results demonstrated that both pedestrians’ overall attitude toward AVs and trust in the effectiveness of AV systems significantly improved after the VR experience. It is also found that the more pedestrians trust the yellow signals, the more likely they are to improve their perception of AVs. Further, pedestrians who exhibit more aggressive crossing behavior are less likely to change their perception towards AVs as compared to those pedestrians who display rule-conforming crossing behaviors. Also, if the experiment made pedestrians feel motion sick, they were less likely to experience increased trust in the AV system\u27s effectiveness

Human Behavior Modeling and Human Behavior-aware Control of Automated Vehicles for Trustworthy Navigation

First and foremost, I would like to thank my advisor, Professor Dawn Tilbury, for her constant guidance and encouragement. She has been extremely helpful in developing my technical, research, and personal skills and immensely supportive of my ideas and endeavors throughout graduate school. She has been an excellent mentor and has always been there in my time of need, encouraging and boosting my confidence when I needed them the most. I would like to specially thank my committee members and collaborators, Professors Lionel Robert and Jessie Yang, for their support and encouragement, right from the start of my graduate program. The multi-disciplinary nature of the research initiated by these three Professors is what first drew me towards pursuing a Ph.D. I would also like to thank my other committee members Professors Ilya Kolmanovsky and Ram Vasudevan, for providing their support and feedback that improved the dissertation. I would like to thank the Department of Mechanical Engineering, Rackham Graduate School, and the University of Michigan for giving me the opportunity to pursue the doctoral degree and providing financial support during my time at the university. In addition, I would like to thank the Toyota Research Institute and the Automotive Research Center for providing financial assistance. I really appreciate the support I received from the MAVRIC lab members. The multi-disciplinary culture and environment that the Professors have fostered in the MAVRIC lab have deeply broadened my perspectives. Specically, I would like to thank Hebert Azevedo-Sa. He is usually the first person I discuss my ideas with and has been an excellent critique. I would also like to thank Connor Esterwood, Na Du, Qiaoning Zhang, and Huajing Zhao for the numerous discussions and help with my user studies; especially Connor, who took on a variety of roles to help with my user study|from an engineer to a tailor, to even a hidden driver. Outside of the University of Michigan, I would like to thank my undergraduate advisor, Professor Madhu M., and my internship advisor at the Indian Institute of Technology-Madras, Professor Saravanan Gurunathan. They encouraged me to pursue research and provided me with the necessary opportunities. A special thanks to Sajaysurya Ganesh, a close friend, and collaborator in my early research projects, with who I discuss ideas even now. Last but not least, I would like to thank my family and friends for supporting me during the past several years. My friends at Ann Arbor made life away from home much easier; they are like my second family. A long list of people from my Master's and Ph.D. programs at the University of Michigan has played an essential role in my graduate experience. Still, I would like to especially thank Sandipp Krishnan Ravi, Subramaniam Balakrishna, Rahasudha Kannan, and Paavai Pari for all their love and support. I will fondly remember my time at the University of Michigan and in Ann Arbor because of all of the people I encountered, the friends I made, and the experiences I had. My parents, wife, and extended family have all been incredibly supportive of the pursuit of my degree, and I am eternally grateful for their love and guidance.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169640/1/jskumaar_1.pd

Modelling vehicle-pedestrian interactions at unsignalised locations employing game-theoretic models

There are some aspects of driver-pedestrian interactions at unsignalised locations that remain poorly understood. Understanding these aspects is vital for promoting road traffic safety in general which involves the interaction of human road users. Recent developments in vehicle automation have called for investigating human-robot interactions before the deployment of highly automated vehicles (HAVs) on roads so that they can communicate effectively with pedestrians making them trustworthy and reliable road users. To understand such interactions, one can simulate interactive scenarios studying various factors affecting road user decision-making processes through lab and naturalistic studies. To quantify such scenarios, mathematical models of human behaviour can be useful. One of these mathematical models that is capable of capturing interactions is game theory (GT). GT can provide valuable insights and strategies to help resolve road user interactions by analysing the behaviour of different participants in traffic situations and suggesting optimal decisions for each party. Thus, the current doctoral thesis aimed to investigate vehicle-pedestrian interactions at unsignalised crossings using GT models, applied to both lab-based and naturalistic data. One of the main aims of the current thesis was to understand how two or more human road users can communicate in a safe and controlled manner demonstrating behaviours of a game-theoretic nature. Thus, an experimental paradigm was created in the form of a distributed simulator study (DSS), by connecting a motion-based driving simulator to a CAVE-based pedestrian simulator to achieve this goal. It was found that the DSS could generate scenarios where participants interact actively showing similar communication patterns to those observed in real traffic. Another prominent finding was the stronger role of vehicle kinematics than personality traits for determining interaction outcomes at unmarked crossings, i.e. whether the pedestrian or driver passed first. To quantify the observations made from the DSS, five computational models namely four GT and one logit model were developed, tested and compared using this dataset. The GT models were obtained from both conventional and behavioural GT literature (CGT and BGT, respectively). This was done to bridge a gap in the previous research, specifically the lack of a comparison between these two modelling approaches in the context of vehicle-pedestrian interactions. Overall, the findings showed that: 1) DSS is a reliable source for the testing and development of GT models; 2) there is a high behaviour variability among road users highlighting the value of studying individualised data in such studies; 3) the BGT models showed promising results in predicting interaction outcomes and simulating the whole interaction process, when compared to the conventional models. These findings suggest that future studies should proceed to adopt, test, and develop BGT approaches for future HAV-human road user interaction studies. To validate the findings of the first two studies, a naturalistic study was conducted in the city of Leeds using state-of-the-art sensors. The sensors gathered road user data including their trajectory and speed over time. The findings from observations revealed similar communication patterns between drivers and pedestrians as in the DSS, suggesting a high degree of relative validity of the experimental paradigm. The results for the computational models were similar but the differences among the models were less noticeable compared to when the models tested against the controlled dataset. Overall, this thesis illustrates that the experimental paradigm and BGT models developed as part of the PhD programme have potential applications for HAV decision-making and motion planning algorithms, as well as traffic safety in general