CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving
Existing adaptive reasoning methods for driving Vision-Language-Action (VLA) models primarily focus on whether to reason, overlooking how reasoning should differ across driving situations. Our key insight is that while scene complexity informs reasoning depth, dynamic risk is equally critical for deciding how to reason in time-critical situations. We therefore propose CAR-VLA, a unified driving VLA model that jointly considers scene complexity and dynamic risk to guide reasoning depth, urgency, and focus. CAR-VLA maps four complexity--risk categories to three reasoning modes: \textit{Fast Intuition} for direct trajectory generation in simple low-risk scenes, \textit{Slow Thinking} for del...
| Authors | Xiaolei Chen, Zhuolin He, Yuxuan Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 September 2026 |
| Last updated at source | 29 September 2026 |
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