Detection and Analysis of Lane Wandering and Cut-Out Scenarios in Naturalistic Driving Data for Automated Driving Safety Assessment

With the focus on data-driven scenario-based methods for the safety assessment of autonomous driving systems, there is a need to generate scenario databases from naturalistic driving data. In this study, we look at the lane wandering and the cut-out scenario, two scenarios that have received little attention to date. The paper is structured as follows: first of all, we model the two scenarios and define parameters necessary to accurately describe them. After that we show how algorithms can be used to detect the scenarios automatically in a large naturalistic driving database recorded on German highways using vehicles equipped with pre-series sensors. Following that, we analyze selected scenario parameters in detail and discuss the findings of the scenario data analysis to derive recommendations for the safety assessment of autonomous driving systems. We were able to show that lane wandering scenarios may in many cases lead to safety critical situations due to the significant lateral velocities and that cut-out scenarios are especially critical if they involve a lane change to the left lane.