SUMO Roundabout Simulation with Human in the Loop
Keywords:SUMO cosimulation, Human in the loop, Driving simulator, Autonomous and connected vehicles
Traffic simulators rely on calibrated driver models in order to reproduce human behavior in different traffic scenarios. Even if quite accurate results can be obtained, the actual interaction between human being and traffic cannot be completely reproduced. In particular, as automated vehicles are being developed, the human in the loop is required to understand whether drivers feel comfortable and safe in mixed traffic conditions. In recent years, dynamic driving simulators have been developed to test vehicles in complex or dangerous situations in safe and controlled environments. However, driving simulators are mostly devoted to the study of vehicle dynamics more than traffic situations.
This paper presents an integration of SUMO with a high end dynamic driving simulator with the aim to study human reactions while negotiating a roundabout in mixed traffic conditions. SUMO is in charge of traffic simulation, while a full vehicle model is employed for the simulation of the dynamic of the human driven car. To allow a human to effectively drive the car, both simulation environments have to run in real time while exchanging the required information. Also, scenario graphics, sound and driving simulator feedback motion have to be accurately realized and synchronized with the simulations. A real-time server is employed for the synchronization of the different environments. As SUMO does not consider vehicle dynamics, particular attention is devoted to the a realistic reconstruction of trajectories and vehicle dynamics to be represented in the scenario.
Some preliminary tests are shown where a panel of testers has been asked to negotiate the roundabout with different percentages of automated vehicles. The results of the tests show that drivers were able to perceive differences in the behavior of other vehicles and that the proposed approach is effective for understanding the feeling of comfort and safety of the human driver.
M. Hasan, D. Perez, Y. Shen, and H. Yang, “Distributed microscopic traffic simulation with human-in-the-loop enabled by virtual reality technologies,” Advances in Engineering Software, vol. 154, pp. 1–16, Dec. 2021. DOI: 1 https://www.doi.org/0.1016/j.advengsoft.2021.102985. DOI: https://doi.org/10.1016/j.advengsoft.2021.102985
S. E. Li, Y. Zheng, K. Li, et al., “Dynamical modeling and distributed control of connected and automated vehicles: Challenges and opportunities,” IEEE Intelligent Transportation Systems Magazine, vol. 9, pp. 46–58, 3 Sep. 2017, ISSN: 19411197. DOI: https://www.doi.org/10.1109/MITS.2017.2709781. DOI: https://doi.org/10.1109/MITS.2017.2709781
H. Zheng, J. Wu, K. Pan, W. Meng, and R. Li, “Research on control target of truck platoon based on maximizing fuel saving rate,” SAE International Journal of Vehicle Dynamics, Stability, and NVH, vol. 4, 2 2020, ISSN: 23802162. DOI: https://www.doi.org/10.4271/10-04-02-0010. DOI: https://doi.org/10.4271/10-04-02-0010
D. Cui, Y. Shen, H. Yang, et al., “Extensible co-simulation framework for supporting cooperative driving automation research,” Transportation Research Record: Journal of the Transportation Research Board, p. 036 119 812 211 212, Sep. 2022, ISSN: 0361-1981.DOI: https://www.doi.org/10.1177/03611981221121263.
X. Zhao, X. Liao, Z.Wang, et al., “Co-simulation platform for modeling and evaluating connected and automated vehicles and human behavior in mixed traffic,” SAE International Journal of Connected and Automated Vehicles, vol. 5, 4 Apr. 2022, ISSN: 2574075X. DOI: 10.4271/12-05-04-0025. DOI: https://doi.org/10.4271/12-05-04-0025
U. E. Manawadu, M. Ishikawa, M. Kamezaki, and S. Sugano, “Analysis of preference for autonomous driving under different traffic conditions using a driving simulator,” Journal of Robotics and Mechatronics, vol. 27, pp. 660–670, 6 2015, ISSN: 18838049. DOI: https://www.doi.org/10.20965/jrm.2015.p0660. DOI: https://doi.org/10.20965/jrm.2015.p0660
S. Espi´e and J. Auberlet, “Joint use of driving simulation and traffic simualtion for the study of road infrastructures and equipments,” in Joint International Conference on Computing and Decision Making in Civil and Building Engineering, 2006, pp. 2554–2563.
I. Vladisavljevic, J. M. Cooper, P. T. Martin, and D. L. Strayer, “Importance of integrating driving and traffic simulations: Case study of impact of cell phone drivers on traffic flow,” in Transportation Research Board 88th Annual Meeting, 2009.
L. Yue, M. Abdel-Aty, and Z. Wang, “Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle,” Journal of Intelligent and Connected Vehicles, vol. 5, pp. 36–45, 1 Feb. 2022, ISSN: 2399-9802. DOI: https://www.doi.org/10.1108/jicv-08-2021-0013 DOI: https://doi.org/10.1108/JICV-08-2021-0013
S. K. Chada, D. Gorges, A. Ebert, R. Teutsch, and C. G. Min, “Learning-based driver behavior modeling and delay compensation to improve the efficiency of an eco-driving assistance system,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Oct. 2022, pp. 415–422, ISBN: 978-1-6654-5258-8. DOI: https://www.doi.org/10.1109/SMC53654.2022.9945577. [Online]. Available: https://ieeexplore.ieee.org/document/9945577/. DOI: https://doi.org/10.1109/SMC53654.2022.9945577
M. Barthauer and A. Hafner, “Testing an adaptive cruise controller with coupled traffic and driving simulations,” in EPiC Series in Computing, SUMO User Conference 2019, vol. 62, 2019, pp. 48–55.
D. Nalic, A. Eichberger, G. Hanzl, M. Fellendorf, and B. Rogic, “Development of a cosimulation framework for systematic generation of scenarios for testing and validation
of automated driving systems,” in 2019 IEEE Intelligent Transportation Systems Conference
M. Barthauer and A. Hafner, “Coupling traffic and driving simulation: Taking advantage of sumo and silab together,” EPiC Series in Engineering, SUMO 2018-Simulating Autonomousand Intermodal Transport Systems, vol. 2, pp. 56–66, 2018.
S. M. Taheri, K. Matsushita, and M. Sasaki, “Virtual reality driving simulation for measuring driver behavior and characteristics,” Journal of Transportation Technologies, vol. 07,pp. 123–132, 02 2017, ISSN: 2160-0473. DOI: https://www.doi.org/10.4236/jtts.2017.72009. DOI: https://doi.org/10.4236/jtts.2017.72009
J. Kaths, B. Schott, and F. Chucholowski, “Co-simulation of the virtual vehicle in virtual traffic considering tactical driver decisions,” in EPiC Series in Computing, SUMO User Conference 2019, 2019, pp. 21–28.
P. A. Lopez, M. Behrisch, L. Bieker-Walz, et al., “Microscopic traffic simulation using sumo,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 2575–2582. DOI: https://www.doi.org/10.1109/ITSC.2018.8569938. DOI: https://doi.org/10.1109/ITSC.2018.8569938
Flow-project. “Flow.” (2019), [Online]. Available: https://flow.readthedocs.io/en/latest/index.html (visited on 04/12/2223).
Politecnico-di-Milano. “Drismi - driving simulator politecnico di milano.” (2022), [Online].Available: https://www.drismi.polimi.it/ (visited on 04/12/2223).
VI-Grade. “Vi-grade: Driving simulator.” (2023), [Online]. Available: https://www.vigrade.com/ (visited on 04/12/2223).
G. Previati, G. Mastinu, and M. Gobbi, “Influence of the inertia parameters on a dynamic driving simulator performances,” in Society of Allied Weight Engineers 81st Annual Conference, 2022, pp. 1–14.
M. Bruschetta, F. Maran, and A. Beghi, “A nonlinear, mpc-based motion cueing algorithm for a high-performance, nine-dof dynamic simulator platform,” IEEE Transactions on Control Systems Technology, vol. 25, pp. 686–694, 2 Mar. 2017, ISSN: 10636536. DOI: https://www.doi.org/10.1109/TCST.2016.2560120. DOI: https://doi.org/10.1109/TCST.2016.2560120
Concurrent-Real-Time. “Concurrent real-time.” (2017), [Online]. Available: https//:concurrent-rt.com/ (visited on 04/12/2223).
Eclipse-Foundation. “Sumo simulation of urban mobility.” (2001), [Online]. Available: https://www.eclipse.org/sumo/ (visited on 04/12/2223).
AI@Edge. “The ai@edge h2020 project.” (2021), [Online]. Available: https://aiatedge.
eu/ (visited on 04/12/2223).
L. Garcíıa Cuenca, J. Sanchez-Soriano, E. Puertas, J. Fernandez Andrés, and N. Aliane,“Machine learning techniques for undertaking roundabouts in autonomous driving,” Sensors, vol. 19, no. 10, 2019, ISSN: 1424-8220. DOI: https://www.doi.org/10.3390/s19102386. [Online]. Available: https://www.mdpi.com/1424-8220/19/10/2386. DOI: https://doi.org/10.3390/s19102386
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