Originally posted on 28 July 2017.

Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances.

Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, execute control over the situation, and respond to changing circumstances inside and outside of the car.

Lex Fridman has worked on autonomous vehicles with companies like Google and Tesla. He recently taught a class on deep learning for semi-autonomous vehicles at MIT, which is freely available online. There was so much ground to cover in this conversation. Most of the conversation was higher level. How do you even approach the problem? What is the hardware and software architecture of a car?

I enjoyed talking to Lex, and if you want to hear more from him check out his podcast Take It Uneasy, which is about jiu jitsu, judo, wrestling, and learning.

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