用预测模型提升自动驾驶车在混行交通中的协同效率
Multi-agent Path Finding for Mixed Autonomy Traffic Coordination
- 基于行为预测与运动基元的搜索算法，融合人类驾驶行为预判
- 在不同自动驾驶渗透率下碰撞率降低，通行延迟显著减少
- 适合研究智能交通系统与多智能体协同控制的学者
在城市交通演进背景下，联网自动驾驶车辆（CAVs）与人类驾驶车辆（HDVs）的融合带来了自主驾驶系统的复杂挑战与机遇。尽管近年来机器人领域发展出针对简化运动模型和完全可控行为的多智能体路径规划（MAPF）算法，但在包含不可控HDVs的混合交通环境中，这些方法难以适用。为此，本文提出行为预测运动优先搜索（BK-PBS），通过离线训练的条件预测模型，提前估计HDV对CAV动作的响应，并将该预测结果融入基于优先级的搜索（PBS）框架，采用A*搜索遍历运动基元以满足车辆动力学约束。在高速公路汇入场景下，对比基于规则的跟驰模型与强化学习算法，BK-PBS在多种自动驾驶渗透率与交通密度条件下均表现出更低的碰撞率与更优的系统级通行延迟。本工作可直接应用于多人类-多机器人协同场景。
原文摘要 · Abstract (English)
In the evolving landscape of urban mobility, the prospective integration of Connected and Automated Vehicles (CAVs) with Human-Driven Vehicles (HDVs) presents a complex array of challenges and opportunities for autonomous driving systems. While recent advancements in robotics have yielded Multi-Agent Path Finding (MAPF) algorithms tailored for agent coordination task characterized by simplified kinematics and complete control over agent behaviors, these solutions are inapplicable in mixed-traffic environments where uncontrollable HDVs must coexist and interact with CAVs. Addressing this gap, we propose the Behavior Prediction Kinematic Priority Based Search (BK-PBS), which leverages an offline-trained conditional prediction model to forecast HDV responses to CAV maneuvers, integrating these insights into a Priority Based Search (PBS) where the A* search proceeds over motion primitives to accommodate kinematic constraints. We compare BK-PBS with CAV planning algorithms derived by rule-based car-following models, and reinforcement learning. Through comprehensive simulation on a highway merging scenario across diverse scenarios of CAV penetration rate and traffic density, BK-PBS outperforms these baselines in reducing collision rates and enhancing system-level travel delay. Our work is directly applicable to many scenarios of multi-human multi-robot coordination.
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