On the Classification of Vehicle Cut‐In Scenarios Severity: Empirical Evidence to Validate the Severity Classification of UN R157

ABSTRACT The Fuzzy Safety Model (FSM), developed amongst all to support UN Regulation No. 157 on Automated Lane Keeping Systems (ALKS), provided a novel methodology for distinguishing between avoidable and non‐avoidable cases of certain test scenarios for Automated Vehicles (AVs). ALKSs, restricted to highway environments, must avoid any reasonably foreseeable and preventable accident. However, beyond this capability, the FSM may also be an optimal tool to classify the difficulty level of the same traffic scenarios. To validate the FSM's ability to classify preventable scenarios according to their difficulty level, a test campaign was conducted focusing on the critical “cut‐in” scenario, where another vehicle changes lanes in front of the ALKS, requiring it to decelerate to avoid a collision. The study demonstrates the feasibility of the required tests and the FSM's effectiveness in categorising preventable cases by difficulty level. Results highlight the model's potential to plan, execute, and analyse cut‐in scenarios beyond the scope of UN R157. This contribution supports the impartial assessment of AVs while addressing the challenge of representing diverse and challenging traffic conditions with a limited number of tests. The research results underscore the FSM's broader applicability for improving AV safety testing frameworks.