Dokania, Neeraj (TU Eindhoven), Singh, Tajinder (Siemens Industry Software Netherlands B.V.), Alirezaei, Mohsen (Siemens Digital Industry), Ploeg, Jeroen (Siemens Industry Software Netherlands B.V.), Lefeber, Erjen (Eindhoven Univ Of Technology)

Implementing a Dissimilarity Metric for Scenarios Categorization and Selection for Automated Driving Systems

Scheduled for presentation during the Regular Session "Vehicle autonomy and connectivity" (TuA2), Tuesday, June 17, 2025, 11:10−11:30, Jos

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

Abstract

The verification and validation of automated vehicles is of utmost importance to ensure road traffic safety. Scenario-based testing is one of the most popular approaches as it is cheaper, safer, and faster than the on-road testing approach. The number of possible scenarios encountered by an automated driving system could be virtually infinite due to the complexity and uncertainty of the driving environment. Hence a framework is needed which expresses the degree of dissimilarity between two driving scenarios quantitatively. This work first develops a dissimilarity metric that compares different driving scenarios and secondly, categorizes them to identify the most critical ones in each category. This way, a set of non-redundant and finite set of scenarios are identified which can be used for validating an automated driving system.