LI AUTO(US:LI) 自动驾驶之心·2025-10-30 00:56Core Insights - The article discusses the advancements in autonomous driving technology, particularly focusing on the transition from data closed-loop systems to training closed-loop systems, marking a new phase in autonomous driving development [17][20]. Group 1: Development of Li Auto's VLA Model - Li Auto's VLA driver model has evolved through various stages, from rule-based systems to AI-driven E2E+VLM systems, with a strong emphasis on navigation as a key module [6]. - The end-to-end mass production version of MPI has reached over 220 units, representing a 19-fold increase compared to the version from July 2024 [12]. Group 2: Data Closed-Loop Value - The data closed-loop process includes shadow mode validation, data mining in the cloud, automatic labeling of effective samples, and model training, with a data return time of one minute [9][10]. - Li Auto has accumulated 1.5 billion kilometers of driving data, utilizing over 200 triggers to produce 15-45 second clip data [10]. Group 3: Transition to Training Closed-Loop - The core of the L4 training loop involves VLA, reinforcement learning (RL), and world models (WM), optimizing trajectories through diffusion and reinforcement learning [22]. - Key technologies for closed-loop autonomous driving training include regional simulation, synthetic data, and reinforcement learning [24]. Group 4: Simulation and Generation Techniques - Simulation relies on scene reconstruction, including visual and Lidar reconstruction, while synthetic data generation utilizes multimodal techniques [25]. - Li Auto's recent advancements in reconstruction and generation have led to significant improvements, with multiple top conference papers published in the last two years [26][29][31]. Group 5: Interactive Agents and System Capabilities - The development of interactive agents is highlighted as a critical challenge in the training closed-loop [37]. - System capabilities are enhanced through world models providing simulation environments, diverse scene construction, and accurate feedback from reward models [38]. Group 6: Community and Collaboration - The article mentions the establishment of nearly a hundred technical discussion groups related to various autonomous driving technologies, with a community of around 4,000 members and over 300 companies and research institutions involved [44][45].