让自动驾驶车在复杂城市中安全绕行行人，结合人群行为模拟与风险感知规划。
Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
- 融合社会力模型与风险感知，模拟真实行人行为并动态调整路径。
- 在高密度人流仿真中实现安全、高效、自适应的车辆轨迹规划。
- 适合研究自动驾驶交互策略或行人避障系统的开发者参考。
在复杂城市环境中，自动驾驶车辆（AV）的运动规划面临巨大挑战。现有研究多聚焦于静态或确定性交通参与者行为，而本文提出一种新算法，通过结合社会力原理模拟真实行人行为，并引入风险感知运动规划器，实现对多人行人在密集场景下的动态响应。我们在二维仿真环境中评估该算法，验证其在高度拥挤的城市环境中能实现安全、高效且自适应的运动规划，为该领域首次达到此性能水平。当前研究未考虑实时性约束，仅在仿真中验证，未来需在完整自动驾驶软件栈及实车平台上进一步测试感知-规划-控制全链路表现。相关代码已开源，可访问 https://github.com/TUM-AVS/PedestrianAwareMotionPlanning 获取。
原文摘要 · Abstract (English)
Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate crowded, unpredictable scenarios with multiple pedestrians while maintaining a safe and efficient vehicle behavior. So far, most research has concentrated on static or deterministic traffic participant behavior. This paper introduces a novel algorithm for motion planning in crowded spaces by combining social force principles for simulating realistic pedestrian behavior with a risk-aware motion planner. We evaluate this new algorithm in a 2D simulation environment to rigorously assess AV-pedestrian interactions, demonstrating that our algorithm enables safe, efficient, and adaptive motion planning, particularly in highly crowded urban environments - a first in achieving this level of performance. This study has not taken into consideration real-time constraints and has been shown only in simulation so far. Further studies are needed to investigate the novel algorithm in a complete software stack for AVs on real cars to investigate the entire perception, planning and control pipeline in crowded scenarios. We release the code developed in this research as an open-source resource for further studies and development. It can be accessed at the following link: https://github.com/TUM-AVS/PedestrianAwareMotionPlanning
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