Multi-agent Path Finding for Mixed Autonomy Traffic Coordination
<|reference_start|>Multi-agent Path Finding for Mixed Autonomy Traffic Coordination: 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.<|reference_end|>
@article{zheng2024multi-agent,
    title={Multi-agent Path Finding for Mixed Autonomy Traffic Coordination},
    author={Han Zheng, Zhongxia Yan, Cathy Wu},
    journal={arXiv preprint arXiv:2409.03881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03881},
    primaryClass={cs.RO cs.AI cs.MA}
}