{"ID":2824219,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23220","arxiv_id":"2512.23220","title":"A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict","abstract":"Human-vehicle cooperative driving serves as a vital bridge to fully autonomous driving by improving driving flexibility and gradually building driver trust and acceptance of autonomous technology. To establish more natural and effective human-vehicle interaction, we propose a Human-Oriented Cooperative Driving (HOCD) approach that primarily minimizes human-machine conflict by prioritizing driver intention and state. In implementation, we take both tactical and operational levels into account to ensure seamless human-vehicle cooperation. At the tactical level, we design an intention-aware trajectory planning method, using intention consistency cost as the core metric to evaluate the trajectory and align it with driver intention. At the operational level, we develop a control authority allocation strategy based on reinforcement learning, optimizing the policy through a designed reward function to achieve consistency between driver state and authority allocation. The results of simulation and human-in-the-loop experiments demonstrate that our proposed approach not only aligns with driver intention in trajectory planning but also ensures a reasonable authority allocation. Compared to other cooperative driving approaches, the proposed HOCD approach significantly enhances driving performance and mitigates human-machine conflict.The code is available at https://github.com/i-Qin/HOCD.","short_abstract":"Human-vehicle cooperative driving serves as a vital bridge to fully autonomous driving by improving driving flexibility and gradually building driver trust and acceptance of autonomous technology. To establish more natural and effective human-vehicle interaction, we propose a Human-Oriented Cooperative Driving (HOCD) a...","url_abs":"https://arxiv.org/abs/2512.23220","url_pdf":"https://arxiv.org/pdf/2512.23220v1","authors":"[\"Qin Wang\",\"Shanmin Pang\",\"Jianwu Fang\",\"Shengye Dong\",\"Fuhao Liu\",\"Jianru Xue\",\"Chen Lv\"]","published":"2025-12-29T05:51:00Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":605558,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2824219,"paper_url":"https://arxiv.org/abs/2512.23220","paper_title":"A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict","repo_url":"https://github.com/i-Qin/HOCD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}