Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data

TL;DR

This paper presents a novel method to identify and evaluate challenging highway scenarios for automated vehicle safety validation using real driving data and a new difficulty metric, independent of vehicle performance.

Contribution

It introduces a hierarchical clustering and rule-based classification approach to extract and assess challenging scenarios from real driving data, independent of vehicle behavior.

Findings

Reduces the number of challenging test scenarios needed

Provides a vehicle-independent evaluation metric

Effectively identifies critical highway scenarios

Abstract

For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (\RDDwo) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assigned to one of nine pre-defined functional scenarios using rule-based classification. The special feature of the subsequent evaluation of the concrete scenarios is that it is independent of the performance of the test vehicle and therefore valid for all AVs. Previous evaluation metrics are often based on the criticality of the scenario, which is, however, dependent on the behavior of the test vehicle and is therefore only conditionally suitable for finding "good" test cases in advance. The results show that with this new approach a reduced number of particularly challenging test scenarios can be derived.