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Driving Validation Challenges

Traditional machine learning excels at optimizing average cases, but the real challenge lies in preparing for extreme scenarios—like reckless pedestrians or unexpected cyclists—that can derail autonomous driving. The concept of a validation set takes on new meaning when considering the need for handcrafted tests that push the limits of machine intelligence. As the discussion unfolds, the focus shifts to how to abstract essential signals and properties that contribute to defensive driving, ensuring safety even in the face of the unpredictable.