| People at Tesla and other autonomous driving companies, of course are aware and worry about such situations. If you have a few hours and want to see many of the technologies and methods that Tesla is using to solve them, check out Tesla's recent "AI day" presentation. Tesla is quite cool about openly discussing the problems they have solved, problems they still have, and how they are trying to solve them. An incomplete list includes: 1) Integrating all the camera views into one 3-D vector space before training the neural network(s). 2) A large in-house group (~1000 people) doing manually labeling of objects in that vector space, not on each camera. 3) Training neural networks for labeling objects. 4) Finding edge cases where the autocar failed (example is when it loses track of a vehicle in front of it when the autocar's view is obscured by a flurry of snow knocked off the roof of the car in front of it), and then querying the large fleet of cars on the road to get back thousands of similar situations to help training. 5) Overlaying multiple views of the world from many cars to get a better vector space mapping of intersections, parking lots, etc 6) New custom build hardware for high speed training of neural nets. 7) Simulations to train rarely encountered situations, like you describe, or very difficult to label situations (like a plaza with 100 people in it or a road in an Indian city). 8) Matching 3-D simulations to what the cars cameras would see using many software techniques. |