Tesla's FSD 12: A Radical Approach to Self-Driving

Tesla's FSD 12 has transitioned to an end-to-end model driven by imitation learning from a deterministic C++ model, improving speed and accuracy. By adopting a neural network model based on videos from top drivers, Tesla avoids the complexity of coding for all corner cases, increasing the likelihood of success exponentially. The model focuses on pixel input for steering, brake, and gas pedal output, enhancing the learning process. The company's radical decision to abandon the old approach for a more elegant, maintainable one showcases their commitment to innovation. Utilizing open-source AI models and massive data uploads from five cameras per car, Tesla fine-tunes models autonomously, resulting in significant monthly improvements. Their focus on outlier data for training ensures consistent model refinement. With infrastructure to process and filter data on the edge, Tesla's autonomous system constantly updates and uploads refined models to cars, leading to exponential advancements. This approach places Tesla far ahead, with their infrastructure and data collection backlog making it challenging for competitors such as Waymo to catch up or adopt the same architecture.