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Safety evaluation of self-driving technologies has been extensively studied.One recent approach uses Monte Carlo based evaluation to estimate theoccurrence probabilities of safety-critical events as safety measures. TheseMonte Carlo samples are generated from stochastic input models constructedbased on real-world data. In this paper, we propose an approach to assess theimpact on the probability estimates from the evaluation procedures due to theestimation error caused by data variability. Our proposed method merges theclassical bootstrap method for estimating input uncertainty with a likelihoodratio based scheme to reuse experiment outputs. This approach is economical andefficient in terms of implementation costs in assessing input uncertainty forthe evaluation of self-driving technology. We use an example in autonomousvehicle (AV) safety evaluation to demonstrate the proposed approach as adiagnostic tool for the quality of the fitted input model.

Evaluation Uncertainty in Data-Driven Self-Driving Testing