Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving

Understand

Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving.

• However, existing VLAs often struggle with achieving efficient inference and generalizing to novel autonomous vehicle configurations and driving scenarios.
• In this paper, we propose Reasoning-VLA, a general and fast action-generation VLA framework.
• The proposed model employs a set of learnable action queries, initialized via Gaussian sampling from ground-truth trajectories within the training corpus.