自动驾驶之心·2025-08-16 16:04Core Viewpoint - The article discusses the challenges and advancements in the deployment of Visual-Language-Action (VLA) models in autonomous driving, emphasizing the integration of 3D spatial understanding with global semantic comprehension. Group 1: Challenges in VLA Deployment - The difficulties in deploying VLA models include multi-modal alignment, data training, and single-chip deployment, but advancements in new chip technologies may alleviate these challenges [2][3][5]. - The alignment issue between Visual-Language Models (VLM) and VLA is gradually being resolved with the release of advanced models like GPT-5, indicating that the alignment is not insurmountable [2][3]. Group 2: Technical Innovations - The VLA model incorporates a unique architecture that combines 3D local spatial understanding with 2D global comprehension, enhancing its ability to interpret complex environments [3][7]. - The integration of diffusion models into VLA is a significant innovation, allowing for improved trajectory generation and decision-making processes [5][6]. Group 3: Comparison with Competitors - The gradual transition from Level 2 (L2) to Level 4 (L4) autonomous driving is highlighted as a strategic approach, contrasting with competitors who may focus solely on L4 from the outset [9][10]. - The article draws parallels between the strategies of different companies in the autonomous driving space, particularly comparing the approaches of Tesla and Waymo [9][10]. Group 4: Future Developments - Future iterations of the VLA model are expected to scale in size and performance, with potential increases in parameters from 4 billion to 10 billion, while maintaining efficiency in deployment [16][18]. - The company is focused on enhancing the model's reasoning capabilities through reinforcement learning, which will play a crucial role in its development [13][51]. Group 5: User Experience and Functionality - The article emphasizes the importance of user experience, particularly in features like voice control and memory functions, which are essential for a seamless interaction between users and autonomous vehicles [18][25]. - The need for a robust understanding of various driving scenarios, including complex urban environments and highway conditions, is crucial for the model's success [22][23]. Group 6: Data and Training - The transition from VLM to VLA necessitates a complete overhaul of data labeling processes, as the requirements for training data have evolved significantly [32][34]. - The use of synthetic data is acknowledged, but the majority of the training data is derived from real-world scenarios to ensure the model's effectiveness [54]. Group 7: Regulatory Considerations - The company is actively engaging with regulatory bodies to ensure that its capabilities align with legal requirements, indicating a proactive approach to compliance [35][36]. - The relationship between technological advancements and regulatory frameworks is highlighted as a critical factor in the deployment of autonomous driving technologies [35][36].