You're putting a lot of faith in the ability to solve a very difficult problem. Computer vision has made huge strides in recent years, but it's still far behind

You're putting a lot of faith in the ability to solve a very difficult problem. Computer vision has made huge strides in recent years, but it's still far behind the capabilities of the human visual cortex. It's not just a matter of building a simple model and training it until it achieves arbitrarily high accuracy. ML encompasses a powerful set of tools, but it's not a magic black box that eats data and spits out AI. We don't yet know how the human brain processes images at a detailed level, and it's too complex of a system to optimize by brute-force. The best ML image processing systems still rely on a lot of pre-encoded assumptions, and require huge amounts of training data/computational resources, and don't perform as well as humans even on simple tasks. For now, autonomous vehicles "cheat" a lot by using sensor technologies like lidar and radar -- techniques that produce spatial data that can be interpreted more easily than raw imagery. That adds a lot to the hardware costs, but it's currently the only way we can make them perform acceptably well in real-world scenarios. And conditions that interfere with those technologies more severely, like fog or snow, are still crippling.