On Why the System Makes the Corner Case: AI-based Holistic Anomaly Detection for Autonomous Driving

Abstract

One big challenge regarding the development of highly automated driving (HAD) functions is validation and, in particular, providing proof of the desired functionality in any given scenario. Especially, corner cases, representing atypical, rare scenarios such as unexpected object movements are of high interest and thus must be detected to treat them with special attention. First, this paper presents a taxonomy for corner cases (CC) with focus on HAD. Specifically, so-called systemic corner cases (SCC) are introduced. Next, a feasibility study is presented on how these SCCs can be detected using different Machine Learning (ML) approaches for anomaly detection. We propose to use a hybrid ensemble of a One-Class Support Vector Machine (OCSVM) and a Clustering-Based Local Outlier Factor (CBLOF) incorporating domain knowledge to account for the nature of corner cases in timely correlated scenarios. The underlying data are unlabeled multivariate time series of HAD-system internal variables. Our experiments on both, synthetically generated and representative real-world CC, show that the hybrid ensemble can detect a variety of real corner cases, which allows for promising validation support of HAD functions.