Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 9
Abstract
Accurately evaluating the safety effects of autonomous vehicles (AVs) has become more pressing with the increased adoption rate of AVs. This study utilizes a multiagent adversarial inverse reinforcement learning (MAAIRL) framework for modeling the interactions between AVs and pedestrians in four different cities: Boston, Las Vegas, Pittsburgh, and Singapore. Multiagent actor-critic with Kronecker factors deep reinforcement learning (MACK DRL), a paradigm that extends deep reinforcement learning (DRL), was used to model the behavior of both AVs and pedestrians and to determine their policies and collision avoidance strategies. Simulated trajectories are compared to actual trajectories and the results are evaluated to analyze the behavior of both AVs and pedestrians in terms of their evasive actions such as swerving, accelerating, or decelerating. The multiagent model provides a more comprehensive insight into how road users act in situations of conflict and accounts for changes in the environment. The study also shows that the level of competition between AVs and pedestrians varies significantly across different cities. Las Vegas has the most competitive relationship between AVs and pedestrians, while Singapore has the least competitive environment. The study also highlights the importance of cooperative behavior, particularly in yielding to pedestrians, in reducing the level of competition between AVs and pedestrians. In summary, this research provides valuable insights into the behavior of AVs and pedestrians and can be used to inform the development of more efficient and safe autonomous mobility systems.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 9 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: May 23, 2023
Accepted: Mar 22, 2024
Published online: Jun 18, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 18, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Case studies
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Equipment and machinery
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Pedestrians and cyclists
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Safety
- Traffic engineering
- Transportation engineering
- Unmanned vehicles
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