Earlier quoted context omitted.
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…
For #1: wouldn't more sensors be even better for training? If vision does not pick up an object like a human, but lidar detects a human sized object, the vision portion of the model can 'learn' from it's mistakes. The other two sound like good reasons though
Obstacle detection with lidar/radar is only interesting if you assume that object avoidance is a problem with camera based obstacle detection right now. There are lots of incidents with Teslas but I don't recall them running over pedestrians or crashing into vehicles a lot. Mostly incidents are about misinterpreting visual signals; not about crashing into stuff. If anything, their safety statistics are pretty good when autopilot is on. The cars still do dangerous/illegal/misguided things due to misinterpreting of traffic situations but it's then smart enough to get the driver out of trouble before bad things happen.