@inproceedings{f151c503a89a421b968dfaa131ac8681,
title = "Genetic Algorithm-Based Test Parameter Optimization for ADAS System Testing",
abstract = "In this paper, we outline the use of a genetic algorithm for test parameter optimization in the context of autonomous and automated driving. Our approach iteratively optimizes test parameters to aim at obtaining critical scenarios that form the basis for virtual verification and validation of Advanced Driver Assistant Systems (ADAS). We consider a test scenario to be critical if the underlying parameter set causes a malfunction of the system equipped with the ADAS function (i.e., near crash or crash of the vehicle). For evaluating the effectiveness of our approach, we set up an automated simulation framework, where we simulated the Euro NCAP car-To-car rear scenario. To assess the criticality of each test scenario we rely on time-To-collision (TTC), which is a well-known and often used time-based safety indicator for recognizing rear-end conflicts. Our genetic algorithm approach showed a higher chance to generate a critical scenario, compared to a random selection of test parameters.",
keywords = "Automatic testing, Autonomous vehicles, Genetic algorithms, System verification",
author = "Florian Kluck and Martin Zimmermann and Franz Wotawa and Mihai Nica",
year = "2019",
month = jul,
day = "1",
doi = "10.1109/QRS.2019.00058",
language = "English",
series = "Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "418--425",
booktitle = "Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019",
address = "United States",
note = "19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 ; Conference date: 22-07-2019 Through 26-07-2019",
}