Tesla(US:TSLA) Hua Er Jie Jian Wen·2025-10-26 05:58Core Argument - Tesla is transforming autonomous driving into a pure AI problem using an "end-to-end" neural network approach, moving away from the traditional modular method that separates perception, prediction, and planning, which they find cumbersome and limited in scalability [1][11]. Group 1: End-to-End Approach - The end-to-end AI model allows the system to directly process pixel data and output driving commands, enabling overall optimization of the entire system [1][11]. - Tesla believes that learning human values from data is easier than encoding them into rules, which is a significant advantage of the end-to-end approach [11][15]. Group 2: Handling Human-Like Decisions - The AI can learn to navigate complex driving scenarios, such as deciding whether to drive through a puddle or into oncoming traffic, by analyzing vast amounts of human driving data [2][15]. - The system can differentiate between groups of animals with different intentions, showcasing its ability to understand "latent intentions" that are difficult to convey in modular systems [3][16]. Group 3: Data Processing and Challenges - Tesla's Full Self-Driving (FSD) system processes up to 2 billion input data points per second, condensing this information into two commands: steering and acceleration [4][20]. - The company leverages a massive data pool, equivalent to 500 years of driving time generated daily by its fleet, to train the AI effectively [22][24]. Group 4: Predictive Capabilities - The AI demonstrates remarkable predictive abilities, such as anticipating a potential collision five seconds before it occurs, which is a level of foresight that traditional systems struggle to achieve [5][24]. Group 5: Interpretability and Evaluation - To address the challenges of debugging the end-to-end model, Tesla outputs interpretable intermediate results alongside driving commands [6][26]. - The company has developed a "neural world simulator" to evaluate the FSD system under various scenarios, allowing for extensive testing and performance assessment [6][37]. Group 6: Technological Versatility - The technology stack developed for FSD is not limited to vehicles; it can also be applied to Tesla's humanoid robot, Optimus, demonstrating its versatility [8][45].