自动驾驶之心·2025-12-14 02:03Core Insights - The article discusses the evolution of autonomous driving technology, highlighting the transition from data closed-loop systems to training closed-loop systems, marking a new phase in autonomous driving development [18][21]. Group 1: Development of Autonomous Driving Technology - The development trajectory of Li Auto's intelligent driving has evolved from rule-based systems to AI-driven E2E+VLM dual systems and VLA, with a focus on navigation as a key module [6]. - Li Auto has accumulated 1.5 billion kilometers of driving data, utilizing over 200 triggers to produce 15-45 second clip data [11]. - The end-to-end mass production version MPI has increased to over 220, representing a 19-fold increase compared to the version from July 2024 [13]. Group 2: Data Closed-Loop and Its Limitations - The data closed-loop process includes shadow mode validation, data mining in the cloud, automatic labeling of effective samples, and model training, with data return achievable in one minute [9][10]. - Despite the effectiveness of the data closed-loop, it cannot address all issues, particularly long-tail scenarios such as traffic control and sudden lane changes [16]. 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 [23]. - Key technologies for closed-loop autonomous driving training include regional simulation, synthetic data, and reinforcement learning [24]. Group 4: Advances in Reconstruction and Generation - Li Auto has made significant advancements in reconstruction and generation, with multiple top conference papers published in the past two years [28][34]. - The company has developed a feedforward 3D generation system that eliminates the need for point cloud initialization, directly producing results from visual inputs [29]. Group 5: Challenges and System Capabilities - The interactive agent is identified as a key challenge in the training closed-loop [40]. - System capabilities are enhanced by the world model providing simulation environments, diverse scene construction, and accurate feedback from reward models [41].