Interesting. Watch at 1/3 speed or so to see it in real time. (Self-driving car videos tend to be published sped up, so you don't see the mistakes.)
The key part of this is, how well does it box everything in the environment? That's the first level of data reduction and the one that determines whether the vehicle hits things. It's doing OK. It's not perfect; it often misses short objects, such as dogs, backpacks on the sidewalk, and once a small child in a group about to cross a street. Fireplugs seem to be misclassified as people frequently. Fixed obstacles are represented as many rectangular blocks, which is fine, and it doesn't seem to be missing important ones. No potholes seen; not clear how well it profiles the pavement. This part of the system is mostly LIDAR and geometry, with a bit of classifier. Again, this is the part of the system essential to not hitting stuff.
This is a reasonable approach. Looks like Google's video from 2017. It's way better than the "dump the video into a neural net and get out steering commands" approach, or the "lane following plus anti-rear-ending, and pretend it's self driving" approach, or the 2D view plane boxing seen from some of the early systems.
Predicting what other road users are going to do is the next step. Once you have the world boxed, you're working with a manageable amount of data. A lot of what happens is still determined by geometry. Can a bike fit in that space? Can the car that's backing up get into the parking space without being obstructed by our vehicle? Those are geometry questions.
Only after that does guessing about human intent really become an issue.