Generating safe and non-conservative behaviors in dense, dynamic environments remains challenging for automated vehicles due to the stochastic nature of traffic participants' behaviors and their implicit interaction with the ego vehicle. This paper presents a novel planning framework, Multipolicy And Risk-aware Contingency planning (MARC), that systematically addresses these challenges by enhancing the multipolicy-based pipelines from both behavior and motion planning aspects. Specifically, MARC realizes a critical scenario set that reflects multiple possible futures conditioned on each semantic-level ego policy. Then, the generated policy-conditioned scenarios are further formulated into a tree-structured representation with a dynamic branchpoint based on the scene-level divergence. Moreover, to generate diverse driving maneuvers, we introduce risk-aware contingency planning, a bi-level optimization algorithm that simultaneously considers multiple future scenarios and user-defined risk tolerance levels. Owing to the more unified combination of behavior and motion planning layers, our framework achieves efficient decision-making and human-like driving maneuvers. Comprehensive experimental results demonstrate superior performance to other strong baselines in various environments.

翻译：在密集动态环境中生成安全且非保守的行为，对自动驾驶车辆而言仍具挑战性，这源于交通参与者行为的随机性及其与自车的隐式交互。本文提出一种新颖的规划框架——多策略与风险感知应急规划（MARC），通过从行为与运动规划两个维度增强基于多策略的流水线，系统性地解决了上述挑战。具体而言，MARC实现了一个关键场景集，该场景集反映了以各语义级自车策略为条件的多种可能未来；进而，基于场景级发散性，将生成的策略条件化场景进一步构建为具有动态分支点的树状表示。此外，为生成多样化驾驶操纵，我们引入了风险感知应急规划——一种双层优化算法，可同时考虑多种未来场景与用户定义的风险容忍水平。得益于行为层与运动规划层的更统一结合，本框架实现了高效决策与人形驾驶操纵。全面的实验结果表明，该方法在多种环境下相较于其他强基线方法具有优越性能。