Phenomenological Modelling of Lane Detection Sensors for Validating Performance of Lane Keeping Assist Systems

A well-established Lane Keeping Assist System (LKAS) plays an important role in the field of Automated Driving (AD). An essential issue in LKAS and generally in Advanced Driving Assist Systems (ADAS) is lane detection. Due to the fact that camera systems are inexpensive, most lane detection methods are vision based. To cope with the infinite number of test cases, virtual testing of ADAS has become state of the art. Realistic behavior and analytical models of ADAS components are crucial for reliable simulation results. The focus of this study is performance validation of LKAS applying simulation. High complexity as well as sensitivity to illumination variation, shadows and different weather conditions make it difficult to implement and develop camera or environment models which could map the realistic behavior of LKAS. To avoid these complexities and minimize the modelling efforts, a phenomenological lane detection model (PLDM) is introduced. For that purpose, comprehensive measurements are carried out within the Austrian Light Vehicle Proving Region for Automated Driving (ALP.Lab) using a test vehicle equipped with LKAS. Applying proposed phenomenological model provides the ability to test any LKAS regardless of its controller. The PLDM is implemented and validated with the recorded data in the simulation environment of IPG CarMaker. The results show realistic system performance of the developed and implemented LKAS system.