Evaluating the Quality of Lane Change Event Detection: Effect of Situational Variables
Merlijne Geurts, J.H. Hogema, Emilia Silvaş, Jan L. Souman, Ashfaqur Rahman, Johannes Hiller · 2022
To develop safe automated driving functions, knowing road-user’s lane change behaviour is critical. This detection problem may depend on multiple aspects such as road conditions, location, and weather. To understand the effect of these situational variables, this work introduces a lane change detection algorithm and assessed its performance under various light conditions, road types and weather conditions. The algorithm has developed in L3Pilot: a large-scale European pilot project on level 3 automation. In the current study, the algorithm was tested with data from a Dutch Field Operational Test on SAE Level 2 systems. The algorithm was assessed against manually annotated video recordings. New is that validation was executed with Dutch Field Operational Test data of different participants and vehicles, distinguishing three situational variables factors. These were day vs night, motorways vs trunk roads and dry vs rain. A bootstrap procedure was used to assess the statistical significance of differences among the conditions. The conclusion is that the algorithm in combination with the provided data is effective in detecting lane changes when data is collected on a sample of Dutch motorways, irrespective of light and precipitation conditions. However, the quality of the sensor signals was worse on trunk roads, yielding significantly worse lane change detection performance (for all light and precipitation conditions).