Work

Implementing a dissimilarity metric for scenarios categorization and selection for automated driving systems

Year: 2025

Type: article

Abstract: To ensure road traffic safety, the safety verification and validation of automated vehicles are of the utmost importance. Scenario-based testing is one of the most popular approaches, as it is cheaper, safer, and faster than on-road testing. 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 finite set of non-redundant scenarios are identified which can be used for validating the safety of an automated driving system.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.096

Authors: Neeraj Dokania, Tajinder Singh, Erjen Lefeber, Jeroen Ploeg, Mohsen Alirezaei

Institutions: Eindhoven University of Technology, Siemens (Germany)

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: HORIZON EUROPE Framework Programme