It's not really tailor made though -- this was a problem researchers faced in the DARPA Urban challenge in 2007, which was held in the desert, and during which

It's not really tailor made though -- this was a problem researchers faced in the DARPA Urban challenge in 2007, which was held in the desert, and during which dust storms frequently confounded sensor systems. LiDAR was the technology that enabled them to complete those task. > A real test of lidar would have been perfectly transparent obstacles. It wouldn't have been realistic, but neither were the other tests. LiDAR would fail that test, which is why you use thermal imaging to see transparent obstacles. Roboticists have known for a long time that orthogonal sensors are the key to building robust systems that perform in a wide variety of environments. > - Cost - Even though the cost can come down quite a bit, the current multi-lidar systems they add both up front cost and maintenance cost. I don't understand why you think the LiDAR system is more expensive. The only evidence you have for camera based systems potentially ever working is that if you squint really hard, eyeballs seem kind of like cameras, and people drive with eyeballs, therefore AI should be able to as well. But you're not factoring in the cost of the AI as part of that system. Your proof of concept requires a human-level intelligence being in control of the car. How is that cheaper than LiDAR? Both in R&D and in the amount of resources it would take to run said AI on the car? > - Complexity - They don't do the same things that cameras do, and can in fact never replace cameras. Again, how is it less complex? If you need an AGI in the mix for it to even get up to the level of systems with LiDAR, how can it be simple? > A Mobileye like solution is likely the best answer here, with a discriminator network to (hopefully) accurately judge which sensor to believe for each detected point, but this is not trivial Huh? This is not typically how robots are made, you don't choose which sensor to believe, you construct a belief and that's informed by various sensors, each of which produce data with some sort of error that accounted for. State estimation allows us to take very untrustworthy and error-filled sensor measurements and produce accurate beliefs. This is why orthogonal sensor modalities are so important to robust system design. You talk about minimizing complexity, but who cares about complexity? What people care about is robustness. Robust/complex solutions are preferred to brittle/simple solutions when human lives are on the line.