Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles
- Journal Article
- DOI: https://doi.org/10.4271/2018-01-1066
Published April 3, 2018 by SAE International in United States
Citation: Hallerbach, S., Xia, Y., Eberle, U., and Koester, F., "Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles," SAE Intl. J CAV 1(2):93-106, 2018, https://doi.org/10.4271/2018-01-1066.
One of the major challenges for the automotive industry will be the release and validation of cooperative and automated vehicles. The immense driving distance that needs to be covered for a conventional validation process requires the development of new testing procedures. Further, due to limited market penetration in the beginning, the driving behavior of other human traffic participants, regarding a mixed traffic environment, will have a significant impact on the functionality of these vehicles.
In this article, a generic simulation-based toolchain for the model-in-the-loop identification of critical scenarios will be introduced. The proposed methodology allows the identification of critical scenarios with respect to the vehicle development process. The current development status of the cooperative and automated vehicle determines the availability of testable simulation models, software, and components.
The identification process is realized by a coupled simulation framework. A combination of a vehicle dynamics simulation that includes a digital prototype of the cooperative and automated vehicle, a traffic simulation that provides the surrounding environment, and a cooperation simulation including cooperative features is used to establish a suitable comprehensive simulation environment. The behavior of other traffic participants is considered in the traffic simulation environment.
The criticality of the scenarios is determined by appropriate metrics. Within the context of this article, both standard safety metrics and newly developed traffic quality metrics are used for evaluation. Furthermore, we will show how the use of these new metrics allows for investigating the impact of cooperative and automated vehicles on traffic. The identified critical scenarios are used as an input for X-in-the-Loop methods, test benches, and proving ground tests to achieve an even more precise comparison to real-world situations. As soon as the vehicle development process is in a mature state, the digital prototype becomes a “digital twin” of the cooperative and automated vehicle.