The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909802126376960 |
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| author | Patrikar, Jay Sharma, Apoorva Veer, Sushant Li, Boyi Scherer, Sebastian Pavone, Marco |
| author_facet | Patrikar, Jay Sharma, Apoorva Veer, Sushant Li, Boyi Scherer, Sebastian Pavone, Marco |
| contents | Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisely the contrastive evidence needed, but they are hard to use: narratives are unstructured, third-person, and poorly grounded to sensor views. We address these challenges by normalizing crash narratives to ego-centric language and converting both logs and crashes into a unified scene-action representation suitable for retrieval. At decision time, our system adjudicates proposed actions by retrieving relevant precedents from this unified index; an agentic counterfactual extension proposes plausible alternatives, retrieves for each, and reasons across outcomes before deciding. On a nuScenes benchmark, precedent retrieval substantially improves calibration, with recall on contextually preferred actions rising from 24% to 53%. The counterfactual variant preserves these gains while sharpening decisions near risk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18626 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving Patrikar, Jay Sharma, Apoorva Veer, Sushant Li, Boyi Scherer, Sebastian Pavone, Marco Robotics Artificial Intelligence Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisely the contrastive evidence needed, but they are hard to use: narratives are unstructured, third-person, and poorly grounded to sensor views. We address these challenges by normalizing crash narratives to ego-centric language and converting both logs and crashes into a unified scene-action representation suitable for retrieval. At decision time, our system adjudicates proposed actions by retrieving relevant precedents from this unified index; an agentic counterfactual extension proposes plausible alternatives, retrieves for each, and reasons across outcomes before deciding. On a nuScenes benchmark, precedent retrieval substantially improves calibration, with recall on contextually preferred actions rising from 24% to 53%. The counterfactual variant preserves these gains while sharpening decisions near risk. |
| title | The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.18626 |