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In order for these vehicles to operate, they must all be surveillance platforms. They are covered in cameras, microphones, radar, sonar, lidar, and other types of sensors. The more of these vehicles are on the roads around you, the more real-time surveillance data is being collected. And you better believe those cameras are doing license plate recognition of every other vehicle they pass, in case there's any incident that would involve an insurance claim. Basically they are mobile Flock on steroids.
Training machine learning models requires data. The edge cases have the least data for training, because they involve unusual weather or road conditions, specific lighting situations, weird/poorly designed intersections, or other uncommon circumstances. Edge cases will never be trained out because there will never be enough data for each specific case to have a meaningful effect on the self-driving model. Edge cases present the highest likelihood of injury to humans, whether as passengers, other drivers, or bystanders.
People also die in droves daily with human drivers. The important point is whether self driving cars are safer than the average human, not whether they eliminate all accidents entirely.