用概率规划提升无控路口人驾车辆的通行安全
Assistive Decision-Making for Right of Way Navigation at Uncontrolled Intersections
- 将路口决策建模为部分可观马尔可夫过程，考虑遮挡与行为不确定性
- 概率规划器在部分可观测下实现最高97.5%无碰撞通行率
- POMCP最重安全，DESPOT兼顾效率与实时可行性，适合实车部署
无控路口因路权规则模糊、遮挡和驾驶行为不可预测，占道路事故相当比例。尽管自动驾驶研究已探索不确定性感知决策，但针对有人驾驶车辆的辅助导航系统仍匮乏。本文提出一种面向无控路口路权推理的驾驶员辅助框架，建模为部分可观马尔可夫决策过程（POMDP）。通过自定义仿真测试平台，包含随机交通参与者、行人、遮挡及对抗性场景，评估四种决策方法：确定性有限状态机（FSM）及三种概率规划器（QMDP、POMCP、DESPOT）。结果表明，概率规划器显著优于基于规则的基线，于部分可观测条件下实现最高97.5%的无碰撞通行率；其中POMCP更注重安全性，DESPOT在效率与运行时可行性间取得平衡。研究凸显了不确定性感知规划对驾驶辅助的重要性，并推动未来融合传感器融合与环境感知模块，实现真实交通环境下的实时部署。
原文摘要 · Abstract (English)
Uncontrolled intersections account for a significant fraction of roadway crashes due to ambiguous right-of-way rules, occlusions, and unpredictable driver behavior. While autonomous vehicle research has explored uncertainty-aware decision making, few systems exist to retrofit human-operated vehicles with assistive navigation support. We present a driver-assist framework for right-of-way reasoning at uncontrolled intersections, formulated as a Partially Observable Markov Decision Process (POMDP). Using a custom simulation testbed with stochastic traffic agents, pedestrians, occlusions, and adversarial scenarios, we evaluate four decision-making approaches: a deterministic finite state machine (FSM), and three probabilistic planners: QMDP, POMCP, and DESPOT. Results show that probabilistic planners outperform the rule-based baseline, achieving up to 97.5 percent collision-free navigation under partial observability, with POMCP prioritizing safety and DESPOT balancing efficiency and runtime feasibility. Our findings highlight the importance of uncertainty-aware planning for driver assistance and motivate future integration of sensor fusion and environment perception modules for real-time deployment in realistic traffic environments.
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