Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR. The optimal sampling point is selected through multi-objective optimization so that the sampling point is close to the decision boundary of the current SVM as well as it has a high uncertainty metric. To illustrate the characteristics and effectiveness of the proposed approach, we apply the proposed approach to both simple problems with synthetic feasible regions and a complex industrial problem: autonomous driving path planning algorithm validation. With the proposed approach, edge cases are more effectively identified, so the classification accuracy of the resulting SVM is considerably higher than that of the SVM trained with conventional sampling techniques such as Monte Carlo Sampling and Latin Hypercube Sampling.
