Part 5/8:
Discussing the structure of the neural networks, Phil pointed out that the input layer captures vast amounts of camera data, IMU (Inertial Measurement Unit) data, GPS data, and more—effectively painting a detailed picture of the vehicle's environment. The output, which controls steering, acceleration, and braking, results from intricate computations through various hidden layers.
This advanced architecture stands in stark contrast to competitors using lidar and coded systems. Tesla’s reliance on a purely end-to-end neural network allows for more naturalistic decision-making processes that mimic human driving behaviors and choices, ultimately creating safer and smoother driving experiences.