single-rb.php

JRM Vol.36 No.3 pp. 779-786
doi: 10.20965/jrm.2024.p0779
(2024)

Paper:

Automatic Lane-Changing System on Congested Highway

Hanwool Woo* ORCID Icon, Hiroto Tetsuka**, and Jongseong Gwak*** ORCID Icon

*Department of Mechanical Systems Engineering, Faculty of Engineering, Kogakuin University
2665-1 Nakano-machi, Hachioji, Tokyo 192-0015, Japan

**Graduate School of Regional Development and Creativity, Utsunomiya University
7-1-2 Yoto, Utsunomiya, Tochigi 321-8585, Japan

***Department of Computer Science, Faculty of Engineering, Takushoku University
815-1 Tatemachi, Hachioji, Tokyo 193-0985, Japan

Received:
February 19, 2024
Accepted:
April 16, 2024
Published:
June 20, 2024
Keywords:
autonomous driving technology, lane change, congested highway, crash avoidance
Abstract

This study proposes an autonomous lane-changing system for congested merging areas. Manual and autonomous vehicles are expected to coexist until all vehicles are substituted by autonomous vehicles. Therefore, interactions between humans and autonomous driving systems should be discussed. This study assumed a scenario in which an autonomous vehicle performed a lane change to a congested main lane, where all vehicles were manual. The proposed system estimated the possibility of changing lanes without collisions. A driving simulator was used to measure the lane-changing operations of human drivers in a congested merging area, and the proposed method was developed based on the experimental results. Simulations demonstrated that the proposed method could safely change lanes.

Lane change to a congested main lane

Lane change to a congested main lane

Cite this article as:
H. Woo, H. Tetsuka, and J. Gwak, “Automatic Lane-Changing System on Congested Highway,” J. Robot. Mechatron., Vol.36 No.3, pp. 779-786, 2024.
Data files:
References
  1. [1] National Highway Traffic Safety Administration of U.S. Department of Transportation, “Lane change/merge crashes: Problem size assessment and statistical description,” Report No.DOT HS 808 075, 1994.
  2. [2] T. Toledo, H. N. Koutsopoulos, and M. E. Ben-Akiva, “Modeling integrated lane-changing behavior,” Transportation Research Record, Vol.1857, No.1, pp. 30-38, 2003. https://doi.org/10.3141/1857-04
  3. [3] K. I. Ahmed, M. E. Ben-Akiva, H. N. Koutsopoulos, and R. G. Mishalani, “Models of freeway lane changing and gap acceptance behavior,” Proc. of the 13th Int. Symp. on Transportation and Traffic Theory, pp. 501-515, 1996.
  4. [4] F. Marczak, W. Daamen, and C. Buisson, “Merging behaviour: Empirical comparison between two sites and new theory development,” Transportation Research Part C: Emerging Technologies, Vol.36, pp. 530-546, 2013. https://doi.org/10.1016/j.trc.2013.07.007
  5. [5] L. Zhao, J. Sun, and H. M. Zhang, “Observations and analysis of multistep-approach lane changes at expressway merge bottlenecks in Shanghai, China,” Transportation Research Record, Vol.2395, No.1, pp. 73-82, 2013. https://doi.org/10.3141/2395-09
  6. [6] H. Yajima and K. Takami, “Inter-vehicle communication protocol design for a yielding decision at an unsignalized intersection and evaluation of the protocol using radio control cars equipped with Raspberry Pi,” Computers, Vol.8, No.1, Article No.16, 2019. https://doi.org/10.3390/computers8010016
  7. [7] Y. Ali, Z. Zheng, M. M. Haque, M. Yildirimoglu, and S. Washington, “Understanding the discretionary lane-changing behaviour in the connected environment,” Accident Analysis & Prevention, Vol.137, Article No.105463, 2020. https://doi.org/10.1016/j.aap.2020.105463
  8. [8] L. Li, J. Gan, K. Zhou, X. Qu, and B. Ran, “A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory,” Physica A: Statistical Mechanics and its Applications, Vol.559, Article No.125039, 2020. https://doi.org/10.1016/j.physa.2020.125039
  9. [9] A. Talebpour, H. S. Mahmassani, and S. H. Hamdar, “Modeling lane-changing behavior in a connected environment: A game theory approach,” Transportation Research Procedia, Vol.7, pp. 420-440, 2015. https://doi.org/10.1016/j.trpro.2015.06.022
  10. [10] Y. Luo, Y. Xiang, K. Cao, and K. Li, “A dynamic automated lane change maneuver based on vehicle-to-vehicle communication,” Transportation Research Part C: Emerging Technologies, Vol.62, pp. 87-102, 2016. https://doi.org/10.1016/j.trc.2015.11.011
  11. [11] L. C. Davis, “Effect of adaptive cruise control systems on traffic flow,” Physical Review E, Vol.69, No.6, Article No.066110, 2004. https://doi.org/10.1103/PhysRevE.69.066110
  12. [12] H. Woo and J. Gwak, “Evaluation of advanced adaptive cruise control based on lane-change detection,” 2022 22nd Int. Conf. on Control, Automation and Systems (ICCAS), pp. 393-396, 2022. https://doi.org/10.23919/ICCAS55662.2022.10003876
  13. [13] V. A. Butakov and P. Ioannou, “Personalized driver/vehicle lane change models for ADAS,” IEEE Trans. on Vehicular Technology, Vol.64, No.10, pp. 4422-4431, 2014. https://doi.org/10.1109/TVT.2014.2369522
  14. [14] L. Wan, P. Raksincharoensak, K. Maeda, and M. Nagai, “Lane change behavior modeling for autonomous vehicles based on surroundings recognition,” Int. J. of Automotive Engineering, Vol.2, No.2, pp. 7-12, 2011. https://doi.org/10.20485/jsaeijae.2.2_7
  15. [15] T. Shamir, “How should an autonomous vehicle overtake a slower moving vehicle: Design and analysis of an optimal trajectory,” IEEE Trans. on Automatic Control, Vol.49, No.4, pp. 607-610, 2004. https://doi.org/10.1109/TAC.2004.825632
  16. [16] V. Milanés and S. E. Shladover, “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data,” Transportation Research Part C: Emerging Technologies, Vol.48, pp. 285-300, 2014. https://doi.org/10.1016/j.trc.2014.09.001
  17. [17] Y. Wang, G. Gunter, M. Nice, M. L. D. Monache, and D. B. Work, “Online parameter estimation methods for adaptive cruise control systems,” IEEE Trans. on Intelligent Vehicles, Vol.6, No.2, pp. 288-298, 2021. https://doi.org/10.1109/TIV.2020.3023674
  18. [18] J. Marzbanrad and I. T. Moghaddam, “Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade,” Vehicle System Dynamics, Vol.54, No.9, pp. 1291-1316, 2016. https://doi.org/10.1080/00423114.2016.1199886
  19. [19] T. Hiraoka, M. Tanaka, H. Kumamoto, T. Izumi, and K. Hatanaka, “Collision risk evaluation index based on deceleration for collision avoidance (first report) – Proposal of a new index to evaluate collision risk against forward obstacles –,” Review of Automotive Engineering, Vol.30, No.4, pp. 429-437, 2009. https://doi.org/10.11351/jsaereview.30.429
  20. [20] C. F. Xing, L. Yang, and Y. H. Zhang, “Study on driver’s reaction time (DRT) during car following,” Applied Mechanics and Materials, Vols.713-715, pp. 2089-2092, 2015. https://doi.org/10.4028/www.scientific.net/AMM.713-715.2089

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024