Risk-Aware Pedestrian Behavior Using Reinforcement Learning in Mixed Traffic

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.