Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim

Energies

Arno Eichberger¹  Demin Nalic¹  Aleksa Pandurevic¹  Martin Fellendorf²  Branko Rogic³ 

[1] Institute of Automotive Engineering, TU Graz, 8010 Graz, Austria;Institute of Highway Engineering and Transport Planning, TU Graz, 8010 Graz, Austria;MAGNA Steyr Fahrzeugtechnik AG Co. & KG, 8045 Graz, Austria;

关键词: automated driving;    scenario-based testing;    software framework;   

DOI  :  10.3390/en14113135

来源: DOAJ

【 摘 要 】

As the complexity of automated driving systemss (ADSs) with automation levels above level 3 is rising, virtual testing for such systems is inevitable and necessary. The complexity of testing these levels lies in the modeling and calculation demands for the virtual environment, which consists of roads, traffic, static and dynamic objects, as well as the modeling of the car itself. An essential part of the safety and performance analysis of ADSs is the modeling and consideration of dynamic road traffic participants. There are multiple forms of traffic flow simulation software (TFSS), which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software environments. In this paper we focus on the TFSS from PTV Vissim in a co-simulation framework which combines Vissim and CarMaker. As it is a commonly used software in industry and research, it also provides complex driver models and interfaces to manipulate and develop customized traffic participants. Using the driver model DLL interface (DMDI) from Vissim it is possible to manipulate traffic participants or adjust driver models in a defined manner. Based on the DMDI, we extended the code and developed a framework for the manipulation and testing of ADSs in the traffic environment of Vissim. The efficiency and performance of the developed software framework are evaluated using the co-simulation framework for the testing of ADSs, which is based on Vissim and CarMaker.

【 授权许可】

Unknown