为自动驾驶设计可应对遮挡风险的动态规划框架，保障安全。
Hope for the Best, Prepare for the Worst: Occlusion-Aware Contingency Planning for Autonomous Vehicles
- 基于可达性分析与树状规划，预判隐藏车辆可能状态
- 在模拟遮挡场景中实现零碰撞，且比传统方法更高效
- 适合高安全要求的自动驾驶系统开发与测试
自动驾驶在城市环境中部署面临重大安全挑战，尤其在存在遮挡的情况下，关键交通参与者可能被隐藏。近期无人驾驶车辆事故凸显了运动规划需明确应对遮挡风险的重要性。本文提出一种形式化的遮挡感知轨迹规划框架，确保即使存在潜在隐藏交通参与者时也能保证避障。基于此前利用可达性分析依次推断隐藏参与者可能状态的方法，本工作集成了一种基于树的运动规划器，能够推理未来观测结果及其缺失情况，从而降低保守性并维持安全保证。在具有挑战性的模拟遮挡场景中验证了该框架的有效性，证明其能主动、高效地保障避碰。
原文摘要 · Abstract (English)
The deployment of autonomous vehicles in urban environments introduces significant safety challenges, particularly in scenarios with occlusions, where critical traffic participants may be hidden from view. Recent accidents involving driverless vehicles highlight the importance of motion planners that explicitly addresses the risks posed by occlusions. In this work, we propose a formal, occlusion-aware trajectory planning framework that guarantees collision avoidance even when there are possible hidden traffic participants. Building on our previous methods that apply reachability analysis to sequentially determine the possible states of hidden traffic participants, we integrate a tree-based motion planner capable of reasoning over future observations and the absence thereof. This approach reduces conservativeness while maintaining safety guarantees. We demonstrate the effectiveness of our framework in a challenging simulated occluded scenario, showing that it pro-actively and efficiently guarantees collision-avoidance.
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