{"ID":2876335,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00981","arxiv_id":"2509.00981","title":"Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles","abstract":"This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving style representation that maps abstract traits to parameters such as speed, safety factors, and reaction time. These parameters are embedded into the MFG through a spatial influence field model. To ensure safe operation in dense traffic, we introduce a safety-critical lane-changing algorithm that leverages dynamic safety margins, time-to-collision analysis, and multi-layered constraints. Real-world NGSIM data is employed for style calibration and empirical validation. Experimental results demonstrate zero collisions across six style combinations, two 15-vehicle scenarios, and NGSIM-based trials, consistently outperforming conventional game-theoretic baselines. Overall, our approach provides a scalable, interpretable, and behavior-aware planning framework for real-world autonomous driving applications.","short_abstract":"This paper presents an MFG-based decision-making framework for autonomous driving in heterogeneous traffic. To capture diverse human behaviors, we propose a quantitative driving style representation that maps abstract traits to parameters such as speed, safety factors, and reaction time. These parameters are embedded i...","url_abs":"https://arxiv.org/abs/2509.00981","url_pdf":"https://arxiv.org/pdf/2509.00981v2","authors":"[\"Liancheng Zheng\",\"Zhen Tian\",\"Yangfan He\",\"Shuo Liu\",\"Huilin Chen\",\"Fujiang Yuan\",\"Yanhong Peng\"]","published":"2025-08-31T20:24:53Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}