AutoDrive-R^2: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving

arXiv:2509.01944v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimodal perception with decision-making capabilities. However, the interpretability and coherence of the decision process and the plausibility of action sequences remain largely underexplored. To address these issues, we propose AutoDrive-R^2, a novel VLA framework that enhances both reasoning and self-reflection capabilities of autonomous driving systems through chain-of-thought (CoT) processing and reinforcement learning (RL). Specifically, we first propose an innovative CoT dataset named nuScenesR^2-6K for supervised fine-tuning, which effectively builds cognitive bridges between input information and output trajectories through a four-step logical chain with self-reflection for validation. Moreover, to maximize both reasoning and self-reflection during the RL stage, we further employ the Group Relative Policy Optimization (GRPO) algorithm within a physics-grounded reward framework that incorporates spatial alignment, vehicle dynamic, and temporal smoothness criteria to ensure reliable and realistic trajectory planning. Extensive evaluation results across both nuScenes and Waymo datasets demonstrates the state-of-the-art performance and robust generalization capacity of our proposed method.

Related

• AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
• Learning Vision-Language-Action World Models for Autonomous Driving
• Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion
• Reflection-Based Task Adaptation for Self-Improving VLA
• Long-Term Memory for VLA-based Agents in Open-World Task Execution

Source: arXiv cs.CV | 2026-04-20