Scenario Reduction of ALKS Development by Using Searching Methods

Abstract

This study presents an approach in Automated Lane Keeping Systems (ALKS) within Automated Driving Systems (ADS), integrating scenario parameterization with Particle Swarm Optimization (PSO) and contrasting it with combinatorial testing (CT). Focusing on critical scenarios vital for ALKS safety, the approach uses UN Regulation 157 to establish a parameter space mirroring real-world driving conditions, ensuring practicality. The integrated parameterization-optimization technique efficiently reduces test scenarios without compromising critical performance aspects and deepens the understanding of system behavior under various conditions. Exploring diverse searching algorithms, particularly CT, enriches ADS development processes. The effective use of PSO in identifying critical scenarios and k-means clustering for directing search efforts highlights the potential of combining multiple methods. This research marks a pivotal step in ADS development, especially in scenario-based testing for ALKS, offering insights for more efficient ADS development and laying the groundwork for future refinements aligned with evolving ADS.

Keywords

• automated driving systems
• scenario parametrization
• automated lane keeping systems
• critical scenario generation
• scenario-based testing

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