To what degree can current ML models and techniques for self-driving understand what's happening based on data captured at multiple time points, maintaining obj

To what degree can current ML models and techniques for self-driving understand what's happening based on data captured at multiple time points, maintaining object permanence? For example, if a a car is stopped at a red light and it sees a child across the street running and the child runs behind a truck it will take her 5 seconds to get past at her last observed rate of speed, and the light turns green at a moment when the car would hit the child as she emerges from behind the truck (given the preceding) as it drives by the truck, do any ML systems have the ability to predict this? People would likely be cautious if they saw the same scene, and would approach slowly. I don't think that current ML systems are able to do that, but would like to hear some informed opinions.