自動運転システム検証のための実走行データからのシナリオ特定
Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems
実走行データをオントロジーで抽象化し、検証対象の論理的な運転シナリオを特定する手法を開発・実装・検証した。
著者: Christian Reichenbächer, Maximilian Rasch, Zafer Kayatas, Florian Wirthmüller, Jochen Hipp, Thao Dang, Oliver Bringmann
分類: cs.RO
原文アブストラクト
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.
