AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

Understand

Recent advancements in Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving by leveraging world knowledge and reasoning capabilities.

• However, current VLA models often struggle with physically infeasible action outputs, complex model structures, or unnecessarily long reasoning.
• In this paper, we propose AutoVLA, a novel VLA model that unifies reasoning and action generation within a single autoregressive generation model for end-to-end autonomous driving.
• AutoVLA performs semantic reasoning and trajectory planning directly from raw visual inputs and language instructions.