Autonomous driving quality assurance with data uncertainty analysis

Deep Learning (DL) based self-driving systems are vigorously developing in big companies. While, as several serious accidents were reported with life- and property-loss, the issue of robustness in DL-based self-driving systems inspires great attention, especially facing with some high-risk cases, like adversarial inputs or corner case scenarios in driving. Considering the existing methods are cumbersome in real driving, therefore this paper proposed a novel and simple way which studies data's uncertainty to rise alarm for manual checking. This method developed a metric describing corner case with respect to DL models, and subsequently evaluated data uncertainty. Experiments on a self-driving system verified the feasibility and usefulness of the proposed method. Code in this paper is released [1].