Clustering-based Criticality Analysis for Testing of Automated Driving Systems

TL;DR

This paper proposes a clustering-based approach to reduce and analyze scenario sets for testing automated driving systems, improving testing efficiency and insight into system behavior.

Contribution

It introduces a novel clustering method to identify and eliminate redundant scenarios, enhancing scenario-based testing for automated driving systems.

Findings

Reduces scenario set size by clustering similar scenarios

Identifies critical and representative scenarios for testing

Provides insights into scenario space and system behavior

Abstract

With the implementation of the new EU regulation 2022/1426 regarding the type-approval of the automated driving system (ADS) of fully automated vehicles, scenario-based testing has gained significant importance in evaluating the performance and safety of advanced driver assistance systems and automated driving systems. However, the exploration and generation of concrete scenarios from a single logical scenario can often lead to a number of similar or redundant scenarios, which may not contribute to the testing goals. This paper focuses on the the goal to reduce the scenario set by clustering concrete scenarios from a single logical scenario. By employing clustering techniques, redundant and uninteresting scenarios can be identified and eliminated, resulting in a representative scenario set. This reduction allows for a more focused and efficient testing process, enabling the allocation of resources to the most relevant and critical scenarios. Furthermore, the identified clusters can provide valuable insights into the scenario space, revealing patterns and potential problems with the system's behavior.