自动驾驶之心·2025-09-03 23:33Core Viewpoint - The article discusses the introduction of AutoDrive-R², a novel Vision-Language-Action (VLA) framework developed by Alibaba and the University of Queensland, aimed at enhancing the reasoning and trajectory planning capabilities of autonomous driving systems through a two-stage training approach [2][49]. Group 1: Framework Overview - AutoDrive-R² integrates a structured reasoning process with self-reflection capabilities to improve decision-making in complex driving scenarios [8][10]. - The framework consists of two training phases: the first phase involves supervised fine-tuning using the nuScenesR²-6K dataset, while the second phase employs reinforcement learning (RL) with a physics-based reward framework [17][49]. Group 2: Dataset and Training - A new dataset, nuScenesR²-6K, was created to facilitate supervised fine-tuning, containing 6,000 "image-trajectory" pairs that include reasoning and self-reflection steps [19][20]. - The training process emphasizes a four-step logical chain: visualization, computation, logic, and reflection, which enhances the model's reasoning capabilities [20][43]. Group 3: Performance and Results - AutoDrive-R² demonstrated state-of-the-art (SOTA) performance on both nuScenes and Waymo datasets, achieving significant reductions in L2 error compared to existing methods [35][37]. - The model's average L2 error on the nuScenes dataset was reduced by 86.9% compared to previous leading methods, showcasing its strong generalization ability [35][39]. Group 4: Reinforcement Learning and Reward Mechanism - The reinforcement learning phase utilizes Group Relative Policy Optimization (GRPO) to optimize trajectory planning, incorporating a physics-based reward framework that ensures the generated trajectories are physically feasible and comfortable [21][26]. - The reward framework includes components for spatial alignment, vehicle dynamics, and temporal smoothness, which collectively guide the model to produce safe and realistic driving strategies [27][30][31]. Group 5: Future Directions - Future research will focus on multi-agent collaboration and real-time sensor fusion integration to further enhance the model's adaptability in complex environments [49].