Perceives but Doesn't Brake: A Perception-Action Binding Gap in Reasoning Driving VLAs
Reasoning-augmented vision-language-action (VLA) driving models can perceive and reason about safety-critical hazards yet fail to translate that evidence into safe actions. We characterize this failure as a perception-action binding gap: hazard information is available inside the model but exerts insufficient control over trajectory generation. Using matched simulated counterfactuals and logged driving replay, we show that in-path hazards are actually encoded in the visual representation, but longitudinal plans are dominated by ego-speed history and respond only weakly to hazard proximity or the model's own reasoning trace. Further analysis shows that the trajectory generator is capable o...
| Authors | Matteo Cercola, Katie Z Luo, Simone Formentin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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