I worked at Starsky Robotics as a perception team intern after graduating high school. I will always be grateful for the team for the opportunity, it was a fantastic first job and everyone who worked there was very kind (especially Stefan).
Unfortunately, Starsky had effectively had no machine learning in 2017 (when I worked there), using solely classical computer vision techniques. This didn't match the company's ambitions of not using LIDAR and there was a strong stigma against switching to a deep learning approach. At the time, very few object detection models had public implementations and I spent a lot of time trying to get a YOLO9000 and RetinaNet implementations running at real-time speeds. Frustrating, as a small startup the labeling services kept screwing us over by returning poorly annotated images.
I think what I took away from the experience is that deep learning in domains with long tails requires a enormous investment in a labeling pipeline - dwarfing the computational aspect - to get decent results. I don't think any solutions are on the horizon that will allow us to bypass this reality. You don't see improvements between Comma.ai and Tesla because it's about the improvements far out on the tail.