ICMAME 2023 Conference Proceedings

Paper No: 112
Paper Title: Scenario-based Parameter Boundary Reduction Approach for Highly Automated Driving Vehicles

Marzana Khatun Electrical Engineering-Functional Safety, Kempten University of Applied Sciences, Kempten, Germany
Heinrich Litagin Mechanical Engineering-Automotive Engineering, Kempten University of Applied Sciences, Kempten, Germany
Rolf Jung Computer Science Engineering, Institute for Driver Assistance and Connected Mobility (IFM), Benningen, Germany
Michael Glaß Embedded Systems / Real - Time Systems, University of Ulm, Ulm, Germany

Scenario-based testing is essential for Highly Automated Driving (HAD) vehicles to determine the safety-related input parameters and their boundaries. The increasing complexity, vehicle functions, and operational design pose new challenges for scenario-based testing, as the number of scenarios is enormous. Therefore, an efficient and systematic process is required in the various stages of scenario-based testing. The contribution of this study is to provide sensitivity information of safety related parameters and support logical scenario reduction. This paper presents an approach that supports to optimize the safety-related parameters boundary towards logical scenario reduction. Additionally, sensitivity analysis is applied by computing Variance- Based Sensitivity Analysis (VBSA) indices and prioritize the input parameters. Two datasets are investigated by VBSA based on the input parameters. One dataset is based on the samples from realworld scenarios and other dataset is derived from the samples considering statistic distributions with a specific parameter range. Moreover, the proposed approach is applied to an exemplary use case and the outcomes are demonstrated.

Keywords: Proportional Integral Derivative (PID), Swarm Intelligence (SI), Renewable Energy (RE), Statistical Analysis

Conference Venue: Mövenpick Hotel & Apartments Bur Dubai, Dubai-UAE
Conference Date: 29-30 April 2023

ISBN Number: 978-625-00-1526-1
DOI Number:

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