Evaluating Learning from Failure in End-to-End Autonomous Driving: A Framework for Safety, Generalization, and Training Cost

End-to-end autonomous driving systems can work well in most scenarios but fail in unique scenarios and under less-than-ideal conditions. What went wrong can enhance safety. Still, if one could learn to perform better after more training, it is not proven that the learning is more effective through failure-oriented training than through a more normal form of continued training. Critical-scenario generation and failure feedback are already employed in existing research, as is counterfactual repair, while reinforcement learning is also already employed. The difficulty of the evaluation challenge will be to distinguish useful learning from further computation, further exposure to similar scenarios, and reduced performance in normal driving. This paper outlines a method for the evaluation of such a comparison. It differentiates failures from near misses, rare events and out-of-distribution events. It splits the distribution that is used to find failures, from the distribution that is used to test the revised policy. There are two complementary comparisons in the framework: one with matched simulator interactions and one with matched computational resources. They both cover failure discovery, scenario generation, verification, and policy adaptation. Generalization is assessed with independent roots of scenarios, unseen families of interactions, and pre-defined combinations of distribution shifts. Assessment of safety outcomes is done in addition to route completion, driving comfort and nominal-performance retention. The paper also outlines appropriate baselines, statistical reporting requirements, and circumstances that would serve as a test to the main hypothesis. No new experiments of training are reported. The framework offers a practical approach to assess the potential for transferable safety gains from learning from failure at reduced adaptation expense.