理想TOP2·2025-10-24 04:48Core Viewpoint - The article discusses the evolution of Li Auto's autonomous driving technology from following Tesla to referencing Tesla, highlighting original innovations made by Li Auto that Tesla has not publicly addressed [2][3]. Group 1: Development Line of Li Auto's Autonomous Driving - Initially, Li Auto's autonomous driving was considered to be following Tesla, but after the introduction of VLM, it transitioned to a reference model, showcasing original innovations not mentioned by Tesla [2]. - The core innovation of Li Auto's VLA is at the DeepSeek MoE level, which is lower than the DeepSeek MLA innovation level [2]. - During the V10-11 period, it was acceptable to say Li Auto was following Tesla, but from V12 onwards, the extent of following has significantly decreased [2]. Group 2: Ashok's Presentation at ICCV 2025 - Ashok Elluswamy discussed Tesla's shift to a single, large end-to-end neural network that directly generates control actions from sensor data, eliminating explicit perception modules [4]. - The reasons for this shift include the difficulty of encoding human values into code, poor interface definitions between traditional perception, prediction, and planning, and the need for scalability to handle real-world complexities [5]. - Key challenges in learning from pixels to control include the curse of dimensionality, interpretability and safety guarantees, and evaluation [6]. Group 3: Solutions to Challenges - To address the curse of dimensionality, Tesla utilizes extensive data from its fleet and employs complex data collection methods to extract valuable corner case data [7]. - For interpretability, end-to-end models can be prompted to predict auxiliary outputs for debugging and safety assurance, with the main focus being on control actions [8]. - The evaluation challenge is addressed through a neural network closed-loop simulator that allows for comprehensive testing and performance assessment [10]. Group 4: Comparison with Li Auto - The article argues that Li Auto's prior announcements on natural language processing and 3D Gaussian representation predate Ashok's presentation, indicating that Li Auto is not merely following Tesla [13]. - The discussion highlights that Ashok's concepts lack groundbreaking ideas, suggesting that Li Auto's innovations are leading rather than following [13]. - The article also notes that Tesla's potential adoption of a VLA-based solution aligns with Li Auto's previously published architecture [16].