Risk‐Aware Pedestrian Behavior Using Reinforcement Learning in Mixed Traffic

• Published in: Computer animation and virtual worlds Vol. 36; no. 3
• Main Authors: Cai, Cheng‐En, Wong, Sai‐Keung, Chen, Tzu‐Yu
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2025; Wiley Subscription Services, Inc
• Subjects: Ablation; mixed traffic; Motorcycles; Pedestrian crossings; pedestrian simulation; Pedestrians; reinforcement learning; risk‐aware assessment; unsignalized environments
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.70031

ABSTRACT This paper introduces a reinforcement learning method to simulate agents crossing roads in unsignalized, mixed‐traffic environments. These agents represent individual pedestrians or small groups. The method ensures that agents adopt safe interactions with nearby dynamic obstacles (bikes, motorcycles, or cars) by considering factors such as conflict zones and post‐encroachment times. Risk assessments based on interaction times encourage agents to avoid hazardous behaviors. Additionally, risk‐informed reward terms incentivize agents to perform safe actions, while collision penalties deter collisions. The method achieved collision‐free crossings and demonstrated normal, conservative, and aggressive pedestrian behaviors in various scenarios. Finally, ablation tests revealed the impact of reward weights, reward terms, and key agent state components. The weights of reward terms can be adjusted to achieve either conservative or aggressive pedestrian crossing behaviors, balancing road crossing efficiency and safety. This paper presents a reinforcement learning‐based approach to simulate pedestrian road‐crossing behavior in mixed traffic environments. Pedestrians assess risks, receive safety‐related rewards, avoid collisions, and display diverse behaviors influenced by adjustments to reward weights.