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arXiv · 2509.18626

The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving

Jay Patrikar·Apoorva Sharma·Sushant Veer·Boyi Li·Sebastian Scherer·Marco Pavone
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Abstract

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.

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Jay Patrikar, Apoorva Sharma, Sushant Veer, Boyi Li, Sebastian Scherer, Marco Pavone. 2025-09-23. The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving. https://arxiv.org/abs/2509.18626

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