Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach.

Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalking risks. In this study, we propose a risk-aware deep reinforcement learning (DRL) approach for AVs to make decisions safely and efficiently in jaywalker-vehicle interactions. Notably, a risk prediction module is incorporated into the traditional DRL framework, making the AV agent risk-aware. Considering the complexity of jaywalker-vehicle conflicts, an encoder-decoder model is adopted as the risk prediction module, which comprehensively integrates multi-source data and predicts probabilities of the final conflict severity levels. The risk-aware DRL approach is applied in a simulated environment established in Anylogic, where the motion features of jaywalkers and vehicles are calibrated using real-world survey data. The trained driving policies are evaluated from perspectives of safety and efficiency across three scenarios with escalading levels of jaywalker volume. Regarding safety performance, the Baseline policy performs the worst in "medium jaywalker volume" scenario and "high jaywalker volume" scenario, while our Proposed risk-aware method outperforms the other methods, with the "low TTC ratio" metric stabilizing near 0.08. Moreover, as the scenario gets more complex, the superiority of our Proposed risk-aware policy gets more evident. In terms of efficiency performance, our Proposed risk-aware policy ranks the second best, achieving an "AV delay" metric around 8.1 s in the "medium jaywalker volume" scenario and 8.5 s in the "high jaywalker volume" scenario. In practice, the proposed risk-aware DRL approach can help AV agents perceive potential risks in advance and navigate through potential jaywalking areas safely and efficiently, further enhancing pedestrian safety.