BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909850527596544 |
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| author | Goldshmidt, Roni Scott, Hamish Niccolini, Lorenzo Zhu, Shizhan Moura, Daniel Zvitia, Orly |
| author_facet | Goldshmidt, Roni Scott, Hamish Niccolini, Lorenzo Zhu, Shizhan Moura, Daniel Zvitia, Orly |
| contents | Existing collision prediction methods often fail to distinguish between ego-vehicle threats and random accidents not involving the ego vehicle, leading to excessive false alerts in real-world deployment. We present BADAS, a family of collision prediction models trained on Nexar's real-world dashcam collision dataset -- the first benchmark designed explicitly for ego-centric evaluation. We re-annotate major benchmarks to identify ego involvement, add consensus alert-time labels, and synthesize negatives where needed, enabling fair AP/AUC and temporal evaluation. BADAS uses a V-JEPA2 backbone trained end-to-end and comes in two variants: BADAS-Open (trained on our 1.5k public videos) and BADAS1.0 (trained on 40k proprietary videos). Across DAD, DADA-2000, DoTA, and Nexar, BADAS achieves state-of-the-art AP/AUC and outperforms a forward-collision ADAS baseline while producing more realistic time-to-accident estimates. We release our BADAS-Open model weights and code, along with re-annotations of all evaluation datasets to promote ego-centric collision prediction research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_14876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data Goldshmidt, Roni Scott, Hamish Niccolini, Lorenzo Zhu, Shizhan Moura, Daniel Zvitia, Orly Computer Vision and Pattern Recognition Existing collision prediction methods often fail to distinguish between ego-vehicle threats and random accidents not involving the ego vehicle, leading to excessive false alerts in real-world deployment. We present BADAS, a family of collision prediction models trained on Nexar's real-world dashcam collision dataset -- the first benchmark designed explicitly for ego-centric evaluation. We re-annotate major benchmarks to identify ego involvement, add consensus alert-time labels, and synthesize negatives where needed, enabling fair AP/AUC and temporal evaluation. BADAS uses a V-JEPA2 backbone trained end-to-end and comes in two variants: BADAS-Open (trained on our 1.5k public videos) and BADAS1.0 (trained on 40k proprietary videos). Across DAD, DADA-2000, DoTA, and Nexar, BADAS achieves state-of-the-art AP/AUC and outperforms a forward-collision ADAS baseline while producing more realistic time-to-accident estimates. We release our BADAS-Open model weights and code, along with re-annotations of all evaluation datasets to promote ego-centric collision prediction research. |
| title | BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.14876 |