Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910014852038656 |
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| author | Xiong, Ziliang Liu, Shipeng Helgesen, Nathaniel Li, Hongwei Johnander, Joakim Forssen, Per-Erik |
| author_facet | Xiong, Ziliang Liu, Shipeng Helgesen, Nathaniel Li, Hongwei Johnander, Joakim Forssen, Per-Erik |
| contents | Collision risk estimation and avoidance play central roles in the safety of autonomous driving (AD) systems. Recently emerged end-to-end AD systems gain collision avoidance ability by minimizing losses to penalize planning trajectories that are too close to other objects. Despite a significant collision rate during testing, most end-to-end planners do not explicitly quantify the collision risk in their outputs. To address this, we introduce RiskMonitor, an efficient plug-and-play module that interprets planning and motion tokens from state-of-the-art end-to-end planners to estimate collision risk. Inspired by loss prediction based uncertainty quantification, RiskMonitor predicts whether the collision loss -- commonly adopted to train end-to-end planners -- is positive along planned waypoints, framing collision risk estimation as a binary classification task. We evaluate RiskMonitor on the real-world nuScenes dataset (open-loop) and the neural-rendering based simulator, NeuroNCAP (closed-loop). Our token-driven method outperforms prediction-driven approaches, including deterministic rules, Gaussian mixture models, and Monte Carlo Dropout. When integrated with a simple braking policy, RiskMonitor improves collision avoidance ability by $66.5\%$ in a closed-loop test on safety-critical scenarios. These results demonstrate that monitoring collision risk using plan and motion tokens enhances the safety of end-to-end AD without retraining it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving Xiong, Ziliang Liu, Shipeng Helgesen, Nathaniel Li, Hongwei Johnander, Joakim Forssen, Per-Erik Robotics Computer Vision and Pattern Recognition Collision risk estimation and avoidance play central roles in the safety of autonomous driving (AD) systems. Recently emerged end-to-end AD systems gain collision avoidance ability by minimizing losses to penalize planning trajectories that are too close to other objects. Despite a significant collision rate during testing, most end-to-end planners do not explicitly quantify the collision risk in their outputs. To address this, we introduce RiskMonitor, an efficient plug-and-play module that interprets planning and motion tokens from state-of-the-art end-to-end planners to estimate collision risk. Inspired by loss prediction based uncertainty quantification, RiskMonitor predicts whether the collision loss -- commonly adopted to train end-to-end planners -- is positive along planned waypoints, framing collision risk estimation as a binary classification task. We evaluate RiskMonitor on the real-world nuScenes dataset (open-loop) and the neural-rendering based simulator, NeuroNCAP (closed-loop). Our token-driven method outperforms prediction-driven approaches, including deterministic rules, Gaussian mixture models, and Monte Carlo Dropout. When integrated with a simple braking policy, RiskMonitor improves collision avoidance ability by $66.5\%$ in a closed-loop test on safety-critical scenarios. These results demonstrate that monitoring collision risk using plan and motion tokens enhances the safety of end-to-end AD without retraining it. |
| title | Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07425 |