All sensors and the systems they operate with have flaws. There is no way around this - it's just the real world. Even cameras will have flaws, or they can deve

All sensors and the systems they operate with have flaws. There is no way around this - it's just the real world. Even cameras will have flaws, or they can develop them over time (lens degradation, image sensor dropping pixels or gaining stuck pixels, etc). That is point behind what is known as "probabilistic robotics" - the real world has noise, and you need to be able to deal with it. Don't expect perfect sensors, don't expect a perfect environment. Self-driving vehicles are the application of probabilistic robotic principles to a real-world task - quite possibly one of the most difficult tasks for the field. To be quite honest, it's amazing how well it's worked in such a short period of time. I think cameras and machine vision approaches will be needed for self-driving vehicles to be fully successful, but I wouldn't say that LIDAR should be counted out. It will probably be necessary - even required - to have all of the sensors currently being used, not just a subset. One kind of sensor that hasn't been explored much that I think will be needed is some kind of audio input; some kind of wide-range microphone (probably binaural or stereo) to take in environmental audio and use that for driving cues. Some simple examples might be for vehicle horns, or the squeal of tires, or the revving up of an engine indicating someone is speeding up aggressively, or an emergency siren, etc. There may even be other sensors needed or that could provide other data to fill in certain gaps of environment knowledge to help a self-driving vehicle navigate. I don't think any one or another should be discounted.