Multi-Modal Traffic Simulation Calibration and Integration with Real-Time Hardware in Loop Simulator
Keywords:Traffic Simulation, Optimization, SUMO
The ongoing research in intelligent transport systems and connected and automated vehicles, enabled by advancements in artificial intelligence, integrating traffic simulations has become an essential part of product/software development for the automotive industry. Nowadays, traffic simulations are used to mimic real-world environment scenarios for virtual testing of advanced transportation technologies. . With the increase in data collection methods for traffic flow, the calibration of the microscopic traffic simulations has emerged as an important research area. The underlying question in traffic modeling is how accurately simulations can mimic the real environment traffic flow conditions? This paper attempts to create a framework for microscopic traffic simulation calibration procedure which can be scaled for large networks. This paper makes the following major contributions. First, a calibration framework is proposed which harnesses the existing data set collected from The Ohio State University (OSU) campus bus service (CABS) busses using Global Positioning System (GPS) sensors to determine the traffic state in the real environment and create a microscopic traffic simulation. The traffic simulation is implemented for a section of the OSU campus (“Woody Hayes Drive") in an opensource traffic simulator – Simulation of Urban MObility (SUMO). The traffic flow generation is probabilistic to introduce variability between scenarios. The second contribution is the development of a communication interface between real-time dSpace ASM Hardware in Loop setup with SUMO to create a complete real-time simulation of urban environments for advanced driver assist systems (ADAS) virtual testing. Ademonstration scenario is the Ohio State University campus network with traffic demand generated using the calibrated model from the first part of the work.
P. Tulpule, S. Midlam-Mohler, A. Karumanchi, and Y. Jin, “A Simulation Tool for Virtual Validation and Verification of Advanced Driver Assistance Systems,” SAE Tech. Pap., no. 2021, pp. 1–9, 2021, doi: https://doi.org/10.4271/2021-01-0865.
R. Dowling, A. Skabardonis, J. Halkias, G. McHale, and G. Zammit, “Guidelines for Calibration of Microsimulation Models: Framework and Applications:,”, no. 1876, pp. 1–9, Jan. 2004,doi: https://doi.org/10.3141/1876-01
H. Singh, S. Midlam-Mohler, and P. Tulpule, “Simulation Based Virtual Testing for Safety of ADAS Algorithms - Case Studies,” SAE Tech. Pap., no. 2021, pp. 1–15, 2021, doi: https://doi.org/10.4271/2021-01-0114.
L. Bieker-Walz, M. Behrisch, M. Junghans, and K. Gimm, “Evaluation of car following-models at controlled intersections,” 31st Annu. Eur. Simul. Model. Conf. 2017, ESM 2017, no. Krauss, pp. 238–243, 2017.
. Hallerbach, Sven, et al. "Simulation-based identification of critical scenarios for cooperative and automated vehicles." SAE International Journal of Connected and Automated Vehicles 1.2018-01-1066 (2018): 93-106
How to Cite
Copyright (c) 2022 Vikhyat Kalra, Punit J Tulpule, Jacob A. Isaman
This work is licensed under a Creative Commons Attribution 3.0 Unported License.