An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Abstract

Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual, new, and potentially dangerous situations. The detection of those situations, which we refer to as corner cases, is highly relevant for successfully developing, applying, and validating automotive perception functions in future vehicles where multiple sensor modalities will be used. A complication for the development of corner case detectors is the lack of consistent definitions, terms, and corner case descriptions, especially when taking into account various automotive sensors. In this work, we provide an application-driven view of corner cases in highly automated driving. To achieve this goal, we first consider existing definitions from the general outlier, novelty, anomaly, and out-of-distribution detection to show relations and differences to corner cases. Moreover, we extend an existing camera-focused systematization of corner cases by adding RADAR (radio detection and ranging) and LiDAR (light detection and ranging) sensors. For this, we describe an exemplary toolchain for data acquisition and processing, highlighting the interfaces of the corner case detection. We also define a novel level of corner cases, the method layer corner cases, which appear due to uncertainty inherent in the methodology or the data distribution.

Causal Models to Support Scenario-Based Testing of ADAS

“…In doing so, we show how a causal model can support scenario-based testing, either by delivering relevant parameter instantiations or by providing a causal rationale for prioritizing test configurations. Prioritization is important when planning a test campaign with limited resources (e.g., time or test infrastructure); with the help of the causal model one can focus on so-called corner or edge cases [11], [35] as candidates for unknown critical situations [12].…”

Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders

“…A central role in safeguarding AI-function for perception in automated driving is being taken by the socalled corner cases, which are rare but mostly highly critical and therefore relevant cases [19,18]. The detection of such corner cases in machine learning (ML) is often referred to as out-of-distribution (OOD) samples [43,36,28] and is an essential building block for safeguarding AI-methods.…”