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

Author

Listed:
  • Demin Nalic

    (Institute of Automotive Engineering, TU Graz, 8010 Graz, Austria)

  • Aleksa Pandurevic

    (Institute of Automotive Engineering, TU Graz, 8010 Graz, Austria)

  • Arno Eichberger

    (Institute of Automotive Engineering, TU Graz, 8010 Graz, Austria)

  • Martin Fellendorf

    (Institute of Highway Engineering and Transport Planning, TU Graz, 8010 Graz, Austria)

  • Branko Rogic

    (MAGNA Steyr Fahrzeugtechnik AG Co. & KG, 8045 Graz, Austria)

Abstract

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.

Suggested Citation

  • Demin Nalic & Aleksa Pandurevic & Arno Eichberger & Martin Fellendorf & Branko Rogic, 2021. "Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim," Energies, MDPI, vol. 14(11), pages 1-9, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3135-:d:563734
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    References listed on IDEAS

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    1. Martin Fellendorf & Peter Vortisch, 2010. "Microscopic Traffic Flow Simulator VISSIM," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 63-93, Springer.
    2. Shuo Feng & Xintao Yan & Haowei Sun & Yiheng Feng & Henry X. Liu, 2021. "Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Demin Nalic & Aleksa Pandurevic & Arno Eichberger & Branko Rogic, 2020. "Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems," Sustainability, MDPI, vol. 12(24), pages 1-12, December.
    4. Kalra, Nidhi & Paddock, Susan M., 2016. "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 182-193.
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    Cited by:

    1. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    2. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    3. Couraud, Benoit & Andoni, Merlinda & Robu, Valentin & Norbu, Sonam & Chen, Si & Flynn, David, 2023. "Responsive FLEXibility: A smart local energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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