Very little of what goes into a current-generation self-driving car is based on machine learning [1]. The reason is exactly your point -- algorithmic approaches to self-driving are much safer and more predictable than a machine learning algorithms.

Instead, LIDAR should exactly identify potential obstacles to the self-driving car on the road. The extent to which machine learning is used is to classify whether each obstacle is a pedestrian, bicyclist, another car, or something else. By doing so, the self-driving car can improve its ability to plan, e.g., if it predicts that an obstacle is a pedestrian, it can plan for the event that the pedestrian is considering crossing the road, and can reduce speed accordingly.

However, the only purpose of this reliance on the machine learning classification should be to improve the comfort of the drive (e.g., avoid abrupt braking). I believe we can reasonably expect that within reason, the self-driving car nevertheless maintains an absolute safety guarantee (i.e., it doesn't run into an obstacle). I say "within reason", because of course if a person jumps in front of a fast moving car, there is no way the car can react. I think it is highly unlikely that this is what happened in the accident -- pedestrians typically exercise reasonable precautions when causing the road.

[1] https://www.cs.cmu.edu/~zkolter/pubs/levinson-iv2011.pdf