Part 5/14:
A crucial insight discussed was the difficulty in programming explicit rules for driving—what Tesla calls "heuristics"—such as how to handle obstacles or interpret intent. For example, decisions like not breaking for an obstacle or passing through puddles are nuanced and context-dependent, varying among drivers and situations.
Elliwami explained that Tesla's system learns these inference patterns intrinsically, capturing human preferences and subtle driving behaviors without cumbersome if-then rules. This end-to-end neural approach allows the system to internalize complex driving nuances that are almost impossible to encode explicitly.