> I encourage our greatest minds to build the tools that will computationally create almost all possible scenarios a driverless vehicle can encounter That's no

> I encourage our greatest minds to build the tools that will computationally create almost all possible scenarios a driverless vehicle can encounter That's not as simple as the writer seems to think it is. Autonomous vehicles can only interface with the world via sensors, and sensor data that looks real, but is actually computationally generated, is one of the best ways to make a model overfit. It can learn the exact function and simplifications a developer has made in order to generate the fake data. Then you can say "alright, record the data and modify it". But you can't actually modify, for instance, point-cloud data that much to account for arbitrary viewpoints. What you can do, and what most companies do afaik, is record the data as a "unit test". The car then responds to the test and, given the result actuation, it is evaluated whether the test passed (e.g.: you want to drive into the sidewalk? test failed). So if the car wants to do a correct maneuver in the test (i.e., one that passes the evaluation), but it is different than the maneuver the test has recorded, then you suddenly don't have the data to assess how well the car did. I know there are ways companies use to solve this but I assume it's not generating data, given the problem presented above. tldr: generated data isnt as straight forward and can lead to bigger problems later on.