No disrespect to anyone, but what he showed in this presentation – which might not even be representative of Mobileye's status quo or potential future – is the complete opposite to how Tesla, comma.ai, Uber and most likely Waymo approach FSD.
- Mobileye's approach, according to this presentation:
Let's divide driving into x sub-tasks / activities / goals and try to come up with a separate mathematical equation or logic for each and everyone of them.
- Everyone else (simplified):
- Let the neural net figure out what driving is and how it works based on provided training data.
- Sandbox the AI and set hard limits ("Under no circumstances do this or that ever." etc.)
- Optional: Have an observer AI judge the driving AI's decision.
- Let it self-improve in simulations and/or shadow driving until it's x-times safer than a human driver in any given situation. Adjust and improve training data, if necessary (go back to step #1, rinse and repeat)
Bottom line:
Mobileye's goal is to build the best-possible robot-like driver agent, everyone else tries to recreate a flawless human-like driver using AI.
Both approaches and goals come with their respective set of pros and cons.
My bet is on deep neural networks.