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arXiv · 2408.09468

Towards Safe and Robust Autonomous Vehicle Platooning: A Self-Organizing Cooperative Control Framework

Chengkai Xu·Zihao Deng·Jiaqi Liu·Aijing Kong·Yu Tang·Chao Huang·Peng Hang
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Abstract

In hybrid traffic environments where human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist, achieving safe and robust decision-making for AV platooning remains a complex challenge. Existing platooning systems often struggle with dynamic formation management and adaptability, especially under complex and dynamic mixed-traffic conditions. To enhance autonomous vehicle platooning within these hybrid environments, this paper presents TriCoD, a twin-world safety-enhanced Data-Model-Knowledge Triple-Driven Cooperative Decision-making Framework. This framework integrates deep reinforcement learning (DRL) with model-driven approaches, enabling dynamic formation dissolution and reconfiguration through a safety-prioritized twin-world deduction mechanism. The DRL component augments traditional model-driven methods, enhancing both safety and operational efficiency, especially under emergency conditions. Additionally, an adaptive switching mechanism allows the system to seamlessly switch between data-driven and model-driven strategies based on real-time traffic demands, thus optimizing decision-making ability and adaptability. Simulation experiments and hardware-in-the-loop tests demonstrate that the proposed framework significantly improves safety, robustness, and flexibility.

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Chengkai Xu, Zihao Deng, Jiaqi Liu, Aijing Kong, Yu Tang, Chao Huang, Peng Hang. 2024-08-18. Towards Safe and Robust Autonomous Vehicle Platooning: A Self-Organizing Cooperative Control Framework. https://arxiv.org/abs/2408.09468

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