ML operates with associative models of billions of parameters: trying to learn thermodynamics by parameterizing for every molecule in a billion images of them.
Animals operate with causal models of a very small number of parameters: these models richly describe how an intervention on one variable causes another to change. These models cannot be inferred from association (hence the last 500 years of science).
They require direct causal intervention in the environment to see how it changes (ie., real learning). And a rich background of historical learning to interpret new observation. You need to have lived a human life to guess what a pedestrian is going to do.
If you overcome the relevant computational infinities to learn "strategy" you will still only do so in the narrow horizon of a highly regulated game where causation has been eliminated by construction (ie., the space of all possible moves over the total horizon of the game can be known in an instant).
The state of all possible (past, current, future) configurations of a physical system cannot be computed -- it's an infinity computational statistics will never bridge.
The solution to self-driving cars will be to try and gamify the roads: robotize people so that machines can understand them. This is already happening on the internet: our behaviour made more machine-like so it can be predicted. I'm sceptical real-world behaviour can be so-constrained.