Corner Cases in Data-Driven Automated Driving: Definitions, Properties and Solutions

Abstract

The field of validation and artificial intelligence (AI) for automated driving has been a rapidly emerging field of research and development in the last few years. Despite the enormous success of machine learning (ML) in perception and robotics, the capability of ML-supported automated driving functions remains to be proven in complex real-world scenarios. Due to stringent regulations and safety concerns, it is crucial to not only be able to identify critical driving events, the corner cases, but also to eliminate them in advance by systematic and provable processes. In contrast to previous work, we analyze and systematize the causes of corner cases from the perspective of neural network interpretation, and consider the network’s performance and robustness in relation to the availability of data points used during development and validation. Moreover, we demonstrate the proposed taxonomy of corner cases on real data from multiple sensor input sources, including images and LiDAR point clouds, showing relevant properties of various corner cases. Furthermore, we discuss the possible solutions dealing with previously unknown classes and driving environments as required in future automated driving use cases.