自动驾驶VLA：OpenDriveVLA、AutoVLA

Core Insights - The article discusses two significant papers, OpenDriveVLA and AutoVLA, which focus on applying large visual-language models (VLM) to end-to-end autonomous driving, highlighting their distinct technical paths and philosophies [22]. Group 1: OpenDriveVLA - OpenDriveVLA aims to address the "modal gap" in traditional VLMs when dealing with dynamic 3D driving environments, emphasizing the need for structured understanding of the 3D world [23]. - The methodology includes several key steps: 3D visual environment perception, visual-language hierarchical alignment, and a multi-stage training paradigm [24][25]. - The model utilizes structured, layered tokens (Agent, Map, Scene) to enhance the VLM's understanding of the environment, which helps mitigate spatial hallucination risks [6][9]. - OpenDriveVLA achieved state-of-the-art performance in the nuScenes open-loop planning benchmark, demonstrating its effective perception-based anchoring strategy [10][20]. Group 2: AutoVLA - AutoVLA focuses on integrating driving tasks into the native operation of VLMs, transforming them from scene narrators to genuine decision-makers [26]. - The methodology features layered visual token extraction, where the model creates discrete action codes instead of continuous coordinates, thus converting trajectory planning into a next-token prediction task [14][29]. - The model employs a dual-mode thinking approach, allowing it to adapt its reasoning depth based on scene complexity, balancing efficiency and effectiveness [28]. - AutoVLA's reinforcement learning fine-tuning (RFT) enhances its driving strategy, enabling the model to optimize its behavior actively rather than merely imitating human driving [30][35]. Group 3: Comparative Analysis - OpenDriveVLA emphasizes perception-language alignment to improve VLM's understanding of the 3D world, while AutoVLA focuses on language-decision integration to enhance VLM's decision-making capabilities [32]. - The two models represent complementary approaches: OpenDriveVLA provides a robust perception foundation, while AutoVLA optimizes decision-making strategies through reinforcement learning [34]. - Future models may combine the strengths of both approaches, utilizing OpenDriveVLA's structured perception and AutoVLA's action tokenization and reinforcement learning to create a powerful autonomous driving system [36].