Multi-agent Path Finding for Mixed Autonomy Traffic Coordination

• URL: http://arxiv.org/abs/2409.03881v1
• Date: Thu, 5 Sep 2024 19:37:01 GMT
• Title: Multi-agent Path Finding for Mixed Autonomy Traffic Coordination
• Authors: Han Zheng, Zhongxia Yan, Cathy Wu,
• Abstract summary: We propose a Behavior Prediction Kinematic Priority Based Search (BK-PBS) to forecast HDV responses to CAV maneuvers. Our work is directly applicable to many scenarios of multi-human multi-robot coordination.
• Score: 7.857093164418706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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