[NovemberWhiskey]

There seems to be gigantic non-sequitur here where good performance of convolutional neural networks for image recognition is seen as evidence that visual sensors suffice for full driving autonomy and that LIDAR-based approaches are heading in the wrong direction.

There doesn't seem any evidence for the proposition that "if only we can train the model with more data it'll suddenly be good enough". There's an extremely long tail of scenarios, operating in different weather conditions, in different environments, at different times of day, and the driving problem is adaptive - the network needs to predict how others will respond to its own behavior.

The authors dismiss the idea that sensor fusion may provide more additive capability than train-with-m0ar-data but don't actually seem to provide any basis for that.