Three reasons: 1) They have hundreds of millions of cars sold already with cameras that they can up-sell this to. That means more revenue. But more importantly

Three reasons: 1) They have hundreds of millions of cars sold already with cameras that they can up-sell this to. That means more revenue. But more importantly, more training data. Massive amounts of it. Training data is the real value here. Radar/lidar/etc. might be able to detect better what is obvious to a human just looking at a thing. But given enough training data, a machine learning can probably replicate that capability so you don't actually need the fancy sensors. Adding new sensors to the mix would set them back quite a while on that front. 2) Simplicity. More sensors means more complexity integrating all the signals and gathering the right training data. More failure modes, etc. It probably also means more compute power needed to process all that data. More complex testing, etc. Scaling by keeping the sensor platform simple is a good move here. 3) The hard part of autonomous driving is actually interpreting visual signals in complex or unusual/rare situations. Roads are designed for humans with eyes. Lidar sees a blob, radar detects a pole, a camera sees a traffic sign, road markings that mean something, etc. It's a much richer signal. All the important stuff on roads is clearly visible. So, cameras are far more important for this than lidar/radar. Those are really great for avoiding crashing into things. Not so much for interpreting and classifying those things. And Tesla seems to be doing pretty OK with not crashing into things. Mostly, the amusing edge cases have to do with misinterpreting visual signals for which radar and lidar are probably not that relevant. It's an interesting approach that they clearly believe that they can make work. It does not actually stop them from later adding more hardware to enhance things if they decide those things are needed. But it's quite interesting how far they are getting with just cameras.