No abstract

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions -and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users -the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.

No abstract

We propose a fast and robust method to estimate the ground surface from LiDAR measurements on an automated vehicle. The ground surface is modeled as a uniform Bspline which is robust towards varying measurement densities and with a single parameter controlling the smoothness prior. We model the estimation process as a robust Least-Squares optimization problem which can be reformulated as a linear problem and thus solved efficiently. Using the SemanticKITTI data set, we conduct a quantitative evaluation by classifying the point-wise semantic annotations into ground and non-ground points. Finally, we validate the approach on our research vehicle in real-world scenarios.