特斯拉最新技术分享，FSD核心架构曝光了

Core Insights - Tesla has publicly shared its FSD (Full Self-Driving) core architecture at the ICCV conference, indicating a significant development in its autonomous driving technology [1][4] - The presentation by Ashok Elluswamy has sparked discussions about Tesla's potential use of VLA (Vision-Language Architecture) in its systems, amidst an ongoing debate in the industry between VLA and world models [1][7] Technical Developments - The FSD architecture integrates a large neural network capable of processing multimodal inputs, including camera video, navigation data, vehicle motion status, and sound, with outputs that include panoramic segmentation, 3D occupancy networks, and language [6][10] - The architecture's ability to output language information suggests a shift towards a more advanced model capable of understanding and reasoning with long-term data [7][10] Industry Context - The debate between VLA and world models is prominent, with VLA proponents arguing for its ability to leverage vast internet data for knowledge accumulation and reasoning, while world model advocates claim it addresses the core challenges of autonomous driving more effectively [7][10] - The industry is moving towards larger model parameters, with Tesla's upcoming smart driving chip expected to reach 2000 TOPS, indicating a significant increase in computational power and model capabilities [10][12] Recent Updates - The latest FSD update (V14.1.3) includes enhancements for safety and personalization, improving obstacle avoidance and navigation capabilities [12] - Tesla has reintroduced the "Mad Max Mode," which allows for a more aggressive driving style, showcasing the system's adaptability in various driving scenarios [11][14]