Description of Corner Cases in Automated Driving: Goals and Challenges
This work tackles the problem of insufficient corner case data for machine learning in automated vehicles, which is crucial for scaling autonomous driving systems, but it is incremental as it builds on existing knowledge-based descriptions.
The paper addresses the challenge of handling corner cases in automated driving, which are rare but critical for safety, and proposes the need for machine-interpretable descriptions to improve dataset analysis and system performance.
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated driving systems are based on machine learning (ML), CC are an essential part of the data for their development. However, there is only a limited amount of CC data in large-scale data collections, which makes them challenging in the context of ML. With a better understanding of CC, offline applications, e.g., dataset analysis, and online methods, e.g., improved performance of automated driving systems, can be improved. While there are knowledge-based descriptions and taxonomies for CC, there is little research on machine-interpretable descriptions. In this extended abstract, we will give a brief overview of the challenges and goals of such a description.