Wei, S., Becker, M., Pfeffer, P. E., & Edelmann, J. (2024). Investigation of the suitability of a static driving simulator for the characterization of Lane Departure Avoidance systems. IEEE Transactions on Intelligent Vehicles, 1–20. https://doi.org/10.34726/7061
Simulation-based development is the general trend in the automotive industry, especially for the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) functions. Driving simulators play an essential role in this by providing a reproducible, safe yet realistic environment for research and development, with the ability to rapidly generate almost infinite system and scenario variants. In this paper, a study has been designed to investigate the suitability of a static driving simulator for characterizing a safety-oriented Lane Departure Avoidance (LDA) system through both subjective and objective assessment. The study comprises two components: a subjective assessment conducted with participants and a standardized driver-in-the-loop test drive to objectively assess the system on the simulator. The research endeavors to determine the most suitable test maneuvers for objectively characterizing the system while also identifying target ranges for relevant objective metrics for an optimal subjective assessment. The results show that the professional drivers give a more reproducible subjective rating than the normal drivers. Notably, both groups consistently evaluate subjective criteria that are based on the perception of the steering wheel movement as well as the change in ego position and heading angle. However, the perception of the absolute ego position does not lead to a consistent subjective assessment. This study suggests that the characterization of Lane Departure Avoidance (LDA) systems on a static driving simulator is generally feasible, with potential for improvement of characterizing aspects based on absolute positions such as maximum lane overshoot.
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Research Areas:
Sustainable and Low Emission Mobility: 70% Automation and Robotics: 30%