Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems

TL;DR

This paper presents a method to identify and abstract driving scenarios from real-world traffic data using qualitative modeling and ontologies, aiding validation of automated driving systems.

Contribution

It introduces a novel approach combining qualitative scenario modeling with ontologies to extract scenarios from field data for automated driving validation.

Findings

Method successfully identifies scenarios in field data

Approach enables scenario probability and shape analysis

Validated with traffic scenario example

Abstract

Scenario-based approaches for the validation of highly automated driving functions are based on the search for safety-critical characteristics of driving scenarios using software-in-the-loop simulations. This search requires information about the shape and probability of scenarios in real-world traffic. The scope of this work is to develop a method that identifies redefined logical driving scenarios in field data, so that this information can be derived subsequently. More precisely, a suitable approach is developed, implemented and validated using a traffic scenario as an example. The presented methodology is based on qualitative modelling of scenarios, which can be detected in abstracted field data. The abstraction is achieved by using universal elements of an ontology represented by a domain model. Already published approaches for such an abstraction are discussed and concretised with regard to the given application. By examining a first set of test data, it is shown that the developed method is a suitable approach for the identification of further driving scenarios.