Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912730548535296 |
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| author | Raiyn, Jamal |
| author_facet | Raiyn, Jamal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics Raiyn, Jamal Robotics Artificial Intelligence 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. |
| title | Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.21280 |