Corner Cases Generation for Virtual Scenario-based Testing of CNN-based Autonomous Driving Function

Abstract:

Deep learning-based perception algorithms have gained importance in autonomous vehicle perception systems in recent years. Since the training data cannot cover all critical scenarios and corner cases， how to ensure the safety and reliability of deep learning-based perception functions in crucial scenarios is still an open challenge. Conventional approaches test the driving functions in real-life environments， which can be risky and uneconomic to validate in corner cases. Virtual scenario-based simulation validation approaches can generate a large number of test cases by setting test scenario parameters， but the purely combinatorial parameter cannot effectively generate corner cases. In this paper， we present a novel approach to generating corner cases in a virtual environment for validation of a CNN （Convolutional Neural Network）-based lane detection function. We represent the scene features with parameters， and then use the deep Q-learning reinforcement learning approach to generate the parameter combinations of corner cases. In addition， by comparing with the approaches of random combination and pairwise combination of scene parameters， our approach can generate corner cases more efficiently and improve the testing efficiency of the autonomous driving perception functions.