Given sufficient data and the compute resources to process that data with a general model, then the general model should be able to converge to a very good prediction (arbitrarily good).
However, it requires sufficient data, which for AI driving could be really large to sufficiently cover all of the driving scenarios that are needed to meet “better than human” safety.
Second however, it then also requires sufficient compute to handle the large, general model and all of the data. The scaling laws show diminishing returns in error improvement for each order of magnitude increase in compute, so reducing the last 9s of driving error could be a real slog.
Third however, a more specialized model with sufficient understanding of the driving process (maybe along the lines of how we can understand the physics of a missile or a tropical storm and employ that knowledge in models to predict the behavior of those systems), could have prediction as good or better than the more general model with less data and less compute.
So, how much data and compute does the general model need for AI driving?
Tesla is learning this right now, or at least we expect that they are.
So second, do we have sufficient knowledge of models of the driving process to reap predictive power in AI driving models?
Waymo and Mobileye believe yes (as best I understand their approaches).