MARC：面向自动驾驶的多策略与风险感知应急规划
MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving
AI总结：
针对自动驾驶在密集动态环境中难以生成安全且非保守行为的问题，提出MARC规划框架，通过构建策略条件场景集与树状结构表示，并结合风险感知应急规划的双层优化算法，实现了高效决策与类人驾驶，性能优于强基线。
摘要：
在密集、动态的环境中生成安全且非保守的行为对自动驾驶车辆而言仍然具有挑战性，这源于交通参与者行为的随机性及其与自车的隐性交互。本文提出了一种新颖的规划框架——多策略与风险感知应急规划（MARC），该框架通过从行为和运动规划两方面增强基于多策略的流水线来系统地应对这些挑战。具体而言，MARC构建了一个关键场景集，该集合反映了以每个语义级自车策略为条件的多种可能未来。随后，生成的策略条件场景被进一步转化为具有动态分支点的树状结构表示，该分支点基于场景级别的差异度。此外，为了生成多样化的驾驶操作，我们引入了风险感知应急规划，这是一种双层优化算法，可同时考虑多种未来场景和用户定义的风险容忍度水平。得益于行为与运动规划层更统一的结合，我们的框架实现了高效的决策和类人驾驶操作。综合实验结果表明，在各种环境中，该框架相较于其他强基线表现出更优的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
