For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is imperative to assess the repercussions of its prospective actions by anticipating the uncertain intentions exhibited by other participants in the traffic environment. Driven by the pronounced multi-modal nature of human driving behavior, this paper presents an approach that leverages Bayesian beliefs over the distribution of potential policies of other road users to construct a novel risk-aware probabilistic motion planning framework. In particular, we propose a novel contingency planner that outputs long-term contingent plans conditioned on multiple possible intents for other actors in the traffic scene. The Bayesian belief is incorporated into the optimization cost function to influence the behavior of the short-term plan based on the likelihood of other agents' policies. Furthermore, a probabilistic risk metric is employed to fine-tune the balance between efficiency and robustness. Through a series of closed-loop safety-critical simulated traffic scenarios shared with human-driven vehicles, we demonstrate the practical efficacy of our proposed approach that can handle multi-vehicle scenarios.

翻译：为使自动驾驶车辆能够在真实交通场景中可靠运行，必须通过预测交通环境中其他参与者表现出的不确定意图来评估其预期行动的潜在后果。基于人类驾驶行为显著的多模态特性，本文提出一种方法，利用对其他道路使用者潜在策略分布的贝叶斯信念，构建新型风险感知概率运动规划框架。具体而言，我们提出一种新型应急规划器，可根据交通场景中其他参与者的多种可能意图输出长期应急规划方案。贝叶斯信念被整合至优化代价函数中，依据其他智能体策略的似然概率影响短期规划行为。此外，采用概率风险度量指标来精细调节效率与鲁棒性之间的平衡。通过一系列与人类驾驶车辆交互的闭环安全关键仿真交通场景实验，我们验证了所提方法处理多车辆场景的实际有效性。