{"ID":2836357,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21280","arxiv_id":"2511.21280","title":"Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics","abstract":"This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC -based approaches.","short_abstract":"This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appr...","url_abs":"https://arxiv.org/abs/2511.21280","url_pdf":"https://arxiv.org/pdf/2511.21280v1","authors":"[\"Jamal Raiyn\"]","published":"2025-11-26T11:11:59Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[]","has_code":false}