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Modeling of Car-to-motorcycle Overtaking Maneuver Based on Comfort Zone Boundaries

Sitthichok Sitthiracha, Saiprasit Koetniyom, Gridsada Phanomchoeng


Thailand has one of the world's highest road fatality rates, mainly on motorcycles. In mixed traffic, motorcycles coexist with other vehicles. The interaction between cars and motorcycles, such as overtaking due to speed differences, can lead to accidents. This scenario also has implications for autonomous vehicles interacting with motorcycles. To increase safety in such interactions, a model was developed that simulates overtaking maneuvers of car drivers with motorcycles, using the concept of comfort zone boundary and a four-phase classification. In a driving simulator, 648 overtaking maneuvers collected from 36 Thai drivers were recorded with different lateral positions and speeds of the motorcycles. A novel graphical method using steering wheel angle and steering wheel velocity signals facilitated the identification of the phases. Time-to-collision and lateral distance characterized driver comfort zones and served as an indicator for safety measures. The lateral position of the motorcycle has proven to be the most influential factor in the model. The results suggest that overtaking vehicles exhibit non-lane-bound driving characteristics and a risk for the sideswipe accident is identified. These results provide a foundational framework for advanced driver assistance systems and motion planning of autonomous vehicles, contributing to improved road safety.


[1] World Health Organization, “Global status report on road safety 2023,” 2023. [Online]. Available: ndle/10665/375016/9789240086517-eng. pdf?sequence=1


[2] T. Litman, “Autonomous vehicle implementation predictions – Implications for transport planning,” Victoria Transport Policy Institute, Canada, 2020.


[3] L. Oliveira, K. Proctor, C. G. Burns, and S. Birrell, “Driving style: How should an automated vehicle behave?,” Information, vol. 10, no. 6, p. 219, 2019, doi: 10.3390/info10060219.


[4] Z. Haruna, M. B. Mu’azu, A. Umar, and G. O. Ufuoma, “Path planning algorithms for mobile robots: A survey,” IntechOpen, Nov. 26, 2023, doi: 10.5772/intechopen.1002655.


[5] A. Tanveer, M. T. Ashraf, and U. Khan, “Motion planning for autonomous ground vehicles using artificial potential fields: A review,” in International Conference on Women Development in Engineering Science & Technology, 2023, doi: arXiv:2310.14339.


[6] K. Chu, M. Lee, and M. Sunwoo, “Local path planning for off-road autonomous driving with avoidance of static obstacles,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1599–1616, Dec. 2012, doi: 10.1109/ tits.2012.2198214.


[7] I. Bae, J. Moon, and J. Seo, “Toward a comfortable driving experience for a self-driving shuttle bus,” Electronics, vol. 8, no. 9, p. 943, Aug. 2019, doi: 10.3390/electronics8090943.


[8] T. Shim, G. Adireddy, and H. Yuan, “Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 226, no. 6, pp. 767–778, Jan. 2012, doi: 10.1177/0954407011430275.


[9] N. M. Negash and J. Yang, “Driver behavior modeling toward autonomous vehicles: Comprehensive review,” IEEE Access, vol. 11, pp. 22788–22821, Jan. 2023, doi: 10.1109/access. 2023.3249144.


[10] S. Mecheri, F. Rosey, and R. Lobjois, “Manipulating constraints on driver-cyclist interactions in a fixed travel space: Effects of road configuration on drivers’ overtaking behavior,” Safety Science, vol. 123, Mar. 2020, Art. no. 104570, doi: 10.1016/j.ssci.2019.104570.


[11] A. Rasch and M. Dozza, “Modeling drivers’ strategy when overtaking cyclists in the presence of oncoming traffic,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–10, 2020, doi: 10.1109/tits.2020.3034679.


[12] A. M. Pérez-Zuriaga, S. Moll, G. López, and A. García, “Driver behavior when overtaking cyclists riding in different group configurations on two-lane rural roads,” International Journal of Environmental Research and Public Health, vol. 18, no. 23, Dec. 2021, Art. no. 12797, doi: 10.3390/ijerph182312797.


[13] F. Feng, S. Bao, R. C. Hampshire, and M. Delp, “Drivers overtaking bicyclists—An examination using naturalistic driving data,” Accident Analysis & Prevention, vol. 115, pp. 98–109, Jun. 2018, doi: 10.1016/j.aap.2018.03.010.


[14] H. Farah, G. Bianchi Piccinini, M. Itoh, and M. Dozza, “Modelling overtaking strategy and lateral distance in car-to-cyclist overtaking on rural roads: A driving simulator experiment,” Transportation Research Part F: Traffic Psychology a nd Behaviour, vol. 63, pp. 226–239, May 2019, doi: 10.1016/j.trf.2019.04.026.


[15] J. Kovaceva, G. Nero, J. Bärgman, and M. Dozza, “Drivers overtaking cyclists in the real-world: Evidence from a naturalistic driving study,” Safety Science, vol. 119, pp. 199–206, Nov. 2019, doi: 10.1016/j.ssci.2018.08.022.


[16] A. Rasch, G. Panero, C.-N. Boda, and M. Dozza, “How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data,” Accident Analysis & Prevention, vol. 139, May 2020, Art. no. 105494, doi: 10.1016/j.aap. 2020.105494.


[17] Y. Xing, C. Lv, H. Wang, H. Wang, Y. Ai, D. Cao, E. Velenis, and F. -Y. Wang, “Driver lane change intention inference for intelligent vehicles: Framework, survey, and challenges,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4377–4390, May 2019, doi: 10.1109/tvt.2019.2903299.


[18] G. Abe, K. Sato, and M. Itoh, “Driver trust in automated driving systems: The case of overtaking and passing,” IEEE Transactions on Human- Machine Systems, vol. 48, no. 1, pp. 85–94, Feb. 2018, doi: 10.1109/thms.2017.2781619.


[19] Y. Xia, Z. Qu, Z. Sun, and Z. Li, “A human-like model to understand surrounding vehicles’ lane changing intentions for autonomous driving,” IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 4178–4189, May 2021, doi: 10.1109/TVT.2021.3073407.


[20] Z. Hao, X. Huang, K. Wang, M. Cui, and Y. Tian, “Attention-based GRU for driver intention recognition and vehicle trajectory prediction,” in 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI), Dec. 2020, doi: 10.1109/cvci51460.2020.9338510.


[21] N. C. Janwani, E. Daş, T. Touma, S. X. Wei, T. G. Molnar, and J. W. Burdick, “A learning-based framework for safe human-robot collaboration with multiple backup control barrier functions,” in Accepted to the International Conference on Robotics and Automation 2024, 2024, doi: 10.48550/arXiv.2310.05865.


[22] M. Syed, J. Sim, N. Mohd, N. Nor, and A. Poi, “Motorcyclists preferred lane position on federal roads in malaysia,” Construction, vol. 3, no. 2, pp. 183–189, Dec. 2023, doi: 10.15282/construc­tion.v3i2.9606.


[23] P. Lemonakis, G. Botzoris, A. Galanis, and N. Eliou, “Speed models for motorcycle riders for two-lane rural roads,” WSEAS Transactions on Environment and Development, vol. 17, pp. 595– 603, May 2021, doi: 10.37394/232015.2021.17.57.


[24] A. Plebe, H. Svensson, S. Mahmoud, and M. D. Lio, “Human-inspired autonomous driving: A survey,” Cognitive Systems Research, vol. 83, pp. 101169–101169, Jan. 2024, doi: 10.1016/j. cogsys.2023.101169.


[25] C.-N. Boda, M. Dozza, K. Bohman, P. Thalya, A. Larsson, and N. Lubbe, “Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?,” Accident Analysis & Prevention, vol. 111, pp. 238–250, Feb. 2018, doi: 10.1016/j.aap.2017.11.032.


[26] B. Gunay, “Methods to quantify the discipline of lane-based-driving,” Traffic Engineering and Control, vol. 44, no. 1, pp. 22–27, Jan. 2003.


[27] G. Phanomchoeng, K. Treetipsounthorn, S. Chantranuwathana, and L. Wuttisittikulkij, “Untripped and tripped rollovers with a neural network,” International Journal of Automotive Technology, vol. 24, no. 3, pp. 811–828, May 2023, doi: 10.1007/s12239-023-0067-9.


[28] P. Petrov and F. Nashashibi, “Modeling and nonlinear adaptive control for autonomous vehicle overtaking,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 4, pp. 1643–1656, Aug. 2014, doi: 10.1109/ tits.2014.2303995.


[29] F. Sagberg, S. Selpi, G. F. B. Piccinini, and J. Engström, “A review of research on driving styles and road safety,” Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 57, no. 7, pp. 1248–1275, Jun. 2015, doi: 10.1177/0018720815591313.


[30] A. Van and J. Hogema, “Time-to-collision and collision avoidance systems,” in 6th ICTCT Workshop, Salzburg, Austria, Jan. 1994, pp. 1–12.


[31] Y. I. Noy, Ergonomics and Safety of Intelligent Driver Interfaces. Florida: CRC Press, 2020.


[32] L. Tong, C. Wang, R. Fu, Y. Ma, Z. Liu, and T. Liu, “Lane-Change risk when the subject vehicle is faster than the following vehicle: A case study on the lane-changing warning model considering different driving styles,” Sustainability, vol. 14, no. 16, pp. 9938–9938, Aug. 2022, doi: 10.3390/ su14169938.


[33] C. Zhao, W. Wang, S. Li, and J. Gong, “Influence of cut-in maneuvers for an autonomous car on surrounding drivers: Experiment and analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2266–2276, Jun. 2020, doi: 10.1109/TITS.2019.2914795.


[34] J. Carmai, S. Koetniyom, W. Sungduang, K. A. A. Kassim, and Y. Ahmad, “Motorcycle accident scenarios and post-crash kinematics of motorcyclists in Thailand,” Journal of the Society of Automotive Engineers Malaysia, vol. 2, no. 3, pp. 231–244, Apr. 2021, doi: 10.56381/jsaem.v2i3.94.

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DOI: 10.14416/j.asep.2024.06.008


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