{"ID":2892672,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15088","arxiv_id":"2507.15088","title":"Search-Based Autonomous Vehicle Motion Planning Using Game Theory","abstract":"In this paper, we propose a search-based interactive motion planning scheme for autonomous vehicles (AVs), using a game-theoretic approach. In contrast to traditional search-based approaches, the newly developed approach considers other road users (e.g. drivers and pedestrians) as intelligent agents rather than static obstacles. This leads to the generation of a more realistic path for the AV. Due to the low computational time, the proposed motion planning scheme is implementable in real-time applications. The performance of the developed motion planning scheme is compared with existing motion planning techniques and validated through experiments using WATonoBus, an electrical all-weather autonomous shuttle bus.","short_abstract":"In this paper, we propose a search-based interactive motion planning scheme for autonomous vehicles (AVs), using a game-theoretic approach. In contrast to traditional search-based approaches, the newly developed approach considers other road users (e.g. drivers and pedestrians) as intelligent agents rather than static...","url_abs":"https://arxiv.org/abs/2507.15088","url_pdf":"https://arxiv.org/pdf/2507.15088v1","authors":"[\"Pouya Panahandeh\",\"Mohammad Pirani\",\"Baris Fidan\",\"Amir Khajepour\"]","published":"2025-07-20T19:02:10Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.GT\"]","methods":"[]","has_code":false}