Part 4/14:
A central topic was Tesla’s move toward a single large end-to-end neural network that processes raw sensor data—video streams, maps, vehicle kinematics, and even audio—and outputs control commands, mainly steering, acceleration, and braking.
Elliwami clarified that Tesla's system no longer employs explicit perception modules (like object detection or lane recognition in isolation). Instead, the entire pipeline is learned as a causal relationship via neural networks, simplifying the process and improving performance. However, he also touched on a "quasi end-to-end" approach, where gradients flow from output to input layers, allowing some modularity but maintaining the core neural network-based architecture.