> but then the challenge is reconciling disagreements with calibrated, and probabilistic fusion

I keep reading arguments like this, but I really don't understand what the problem here is supposed to be. Yes, in a rule based system, this is a challenge, but in an end-to-end neural network, another sensor is just another input, regardless of whether it's another camera, LIDAR, or a sensor measuring the adrenaline level of the driver.

If you have enough training data, the model training will converge to a reasonable set of weights for various scenarios. In fact, training data with a richer set of sensors would also allow you to determine whether some of the sensors do not in fact contribute meaningfully to overall performance.