VeriDrive: Verifiable Counterfactual Supervision for Cost-Efficient Vision-Language Planning
Vision-language driving models increasingly use reasoning supervision to bridge perception, prediction, and planning, but existing driving rationales are often free-form and expensive to generate with frontier models. We present VeriDrive, a framework for constructing planning-oriented, verifiable counterfactual supervision. VeriDrive converts driving reasoning into a structured Perception-Evaluation-Revision chain that grounds key objects in future motion, evaluates alternative ego trajectories with rule-checkable evidence, revises risky intent toward expert behavior, and produces final planning targets. To scale data construction, VeriDrive combines local generation with validator-guide...
| Authors | Zikai Zhang, Hubert P. H. Shum, Toby P. Breckon |
|---|---|
| Primary category | cs.CV |
| Published | 5 June 2026 |
| Last updated at source | 20 September 2026 |
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