There is a fundamental disconnect between what deep vision models can do and what is expected of them. On the one hand, there is a very good reason why mean-ave

There is a fundamental disconnect between what deep vision models can do and what is expected of them. On the one hand, there is a very good reason why mean-average-precision is used to assess detection-classification models: because even people make mistakes. On the other hand, we need to apply the use of these forever imperfect models with care, context, and redundancy. This is why engineers add a dozen other input types to ADAS systems in addition to vision (sonar, lidar, mesh computing, etc). This is why regulation is needed, to prevent less rigorous products from making their way into situations where the can be easily compromised, or worse, deadly.