But that's the issue: identifying a pedestrian vs a snowman or a mailbox or a cardboard cutout is important when deciding whether to swerve left or right. It's an asymptotic problem: you'll never get 100% identification, and based on that, even the rigid algorithms will make mistakes.
LIDAR is also not perfect when the road is covered in 5 inches of snow and you can't tell where the lanes are. Or at predicting a driver that's going to swerve into your lane because they spilled coffee on their lap or had a stroke.
With erratic input, you will get erratic output. Even the best ML vision algorithm will sometimes produce shit output, which will become input to the actual driving algorithm.
I can see when the car in front of me is acting erratic or notice when the driver next to me is talking on their phone and adjust my following distance automatically. I don't think self-driving cars are at that point yet. The rules for driving a car on a road is fairly straightforward - predicting what humans will do -- that's far from trivial and we've had many generations of genetic algorithms working on that problem.
Self-driving cars could compensate for that with reaction time. Think of it this way: you trying to predict what the other driver will do is partly compensating for your lack of reaction time. A self-driving car could, in worst-case scenario, treat the other car as randomly-moving car-shaped object, compute the envelope of its possible moves, and make sure to stay out of it.
Normal cars could do this too. Higher end luxury cars already started using the parking sensors to automatically apply the brakes way before you do if something is in front of the car and approaching fast. If this was really that easy, then we wouldn't have all these accidents reported about self driving cars: the first line of your event loop would just be `if (sensors.front.speed < -10m/s) {brakes.apply()}` and Teslas and Ubers wouldn't hit slow moving objects ever. I suspect that's not really how this works though.
LIDAR is also not perfect when the road is covered in 5 inches of snow and you can't tell where the lanes are. Or at predicting a driver that's going to swerve into your lane because they spilled coffee on their lap or had a stroke.
With erratic input, you will get erratic output. Even the best ML vision algorithm will sometimes produce shit output, which will become input to the actual driving algorithm.