{"ID":2875660,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03550","arxiv_id":"2509.03550","title":"Diffusion-RL Based Air Traffic Conflict Detection and Resolution Method","abstract":"In the context of continuously rising global air traffic, efficient and safe Conflict Detection and Resolution (CD\u0026R) is paramount for air traffic management. Although Deep Reinforcement Learning (DRL) offers a promising pathway for CD\u0026R automation, existing approaches commonly suffer from a \"unimodal bias\" in their policies. This leads to a critical lack of decision-making flexibility when confronted with complex and dynamic constraints, often resulting in \"decision deadlocks.\" To overcome this limitation, this paper pioneers the integration of diffusion probabilistic models into the safety-critical task of CD\u0026R, proposing a novel autonomous conflict resolution framework named Diffusion-AC. Diverging from conventional methods that converge to a single optimal solution, our framework models its policy as a reverse denoising process guided by a value function, enabling it to generate a rich, high-quality, and multimodal action distribution. This core architecture is complemented by a Density-Progressive Safety Curriculum (DPSC), a training mechanism that ensures stable and efficient learning as the agent progresses from sparse to high-density traffic environments. Extensive simulation experiments demonstrate that the proposed method significantly outperforms a suite of state-of-the-art DRL benchmarks. Most critically, in the most challenging high-density scenarios, Diffusion-AC not only maintains a high success rate of 94.1% but also reduces the incidence of Near Mid-Air Collisions (NMACs) by approximately 59% compared to the next-best-performing baseline, significantly enhancing the system's safety margin. This performance leap stems from its unique multimodal decision-making capability, which allows the agent to flexibly switch to effective alternative maneuvers.","short_abstract":"In the context of continuously rising global air traffic, efficient and safe Conflict Detection and Resolution (CD\u0026R) is paramount for air traffic management. Although Deep Reinforcement Learning (DRL) offers a promising pathway for CD\u0026R automation, existing approaches commonly suffer from a \"unimodal bias\" in their po...","url_abs":"https://arxiv.org/abs/2509.03550","url_pdf":"https://arxiv.org/pdf/2509.03550v1","authors":"[\"Tonghe Li\",\"Jixin Liu\",\"Weili Zeng\",\"Hao Jiang\"]","published":"2025-09-02T23:17:46Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Diffusion Model\"]","has_code":false}