End-to-End Learning

End-to-end learning is a key innovation in Tesla's approach. Instead of breaking down the driving task into separate modules (perception, planning, control), Tesla trains neural networks to learn the entire mapping from sensor inputs directly to control outputs.

This approach: - Reduces engineering complexity - Allows the system to learn optimal behaviors - Can discover solutions humans might not think of - Requires massive amounts of training data

The challenge is ensuring safety and interpretability, which Tesla addresses through: - Extensive simulation and testing - Fleet data collection - Gradual rollout with human oversight - Continuous monitoring and improvement