Authors:
Merlijne Geurts
1
;
2
;
Jeroen Hogema
2
;
Emilia Silvas
1
;
2
;
Jan Souman
2
;
Ashfaqur Rahman
3
and
Johannes Hiller
4
Affiliations:
1
Control Systems Technology, Eindhoven University of Technology, Eindhoven, The Netherlands
;
2
Integrated Vehicle Safety, TNO, Helmond, The Netherlands
;
3
Assisted & Automated Driving, Jaguar Land Rover, Gaydon, U.K.
;
4
Vehicle Intelligence & Automated Driving, Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany
Keyword(s):
Event Detection, Vehicle Driving, Intelligent Vehicles, Lane Change Detection.
Abstract:
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 was 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 signific
ance 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).
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