Share:


Rear-end collision escape algorithm for intelligent vehicles supported by vehicular communication

    Chentong Bian Affiliation
    ; Guodong Yin Affiliation
    ; Liwei Xu Affiliation
    ; Ning Zhang Affiliation

Abstract

To reduce rear-end collision risks and improve traffic safety, a novel rear-end collision escape algorithm is proposed for intelligent vehicles supported by vehicular communication. Numerous research has been carried out on rear-end collision avoidance. Most of these studies focused on maintaining a safe front clearance of a vehicle while only few considered the vehicle’s rear clearance. However, an intelligent vehicle may be collided by a following vehicle due to wrong manoeuvres of an unskilled driver of the following vehicle. Hence, it is essential for an intelligent vehicle to maintain a safe rear clearance when there is potential for a rear-end collision caused by a following vehicle. In this study, a rear-end collision escape algorithm is proposed to prevent rear-end collisions by a following vehicle considering both straight and curved roads. A trajectory planning method is designed according to the motions of the considered intelligent vehicle and the corresponding adjacent vehicles. The successive linearization and the Model Predictive Control (MPC) algorithms are used to design a motion controller in the proposed algorithm. Simulations were performed to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm is effective in preventing rear-end collisions caused by a following vehicle.


First published online 18 January 2023

Keyword : rear-end collision, collision avoidance, model predictive control, intelligent vehicle, traffic safety, vehicular communication

How to Cite
Bian, C., Yin, G., Xu, L., & Zhang, N. (2022). Rear-end collision escape algorithm for intelligent vehicles supported by vehicular communication. Transport, 37(6), 398–410. https://doi.org/10.3846/transport.2022.18172
Published in Issue
Dec 31, 2022
Abstract Views
352
PDF Downloads
603
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Andersen, G. J.; Sauer, C. W. 2007. Optical information for car following: the driving by visual angle (DVA) model, Human Factors: the Journal of the Human Factors and Ergonomics Society 49(5): 878–896. https://doi.org/10.1518/001872007X230235

Benedetto, F.; Calvi, A.; D’Amico, F.; Giunta, G. 2015. Applying telecommunications methodology to road safety for rear-end collision avoidance, Transportation Research Part C: Emerging Technologies 50: 150–159. https://doi.org/10.1016/j.trc.2014.07.008

Birgin, E. G.; Mario Martínez, J. 2002. Large-scale active-set box-constrained optimization method with spectral projected gradients, Computational Optimization and Applications 23(1): 101–125. https://doi.org/10.1023/A:1019928808826

Camacho, E. F.; Bordons, C. 2007. Model Predictive Control. Springer. 405 p. https://doi.org/10.1007/978-0-85729-398-5

Chen, C.; Liu, H.; Xiang, H.; Li, M.; Pei, Q.; Wang, S. 2016. A rear-end collision avoidance scheme for intelligent transportation system, MATEC Web of Conferences 81: 02001. https://doi.org/10.1051/matecconf/20168102001

Deng, T. 2000. Research on Feedback Correction and Greenhouse Control Using Predictive Control. Tongji University, China. (in Chinese).

Eskandarian, A. 2012. Handbook of Intelligent Vehicles. Springer. 1599 p. https://doi.org/10.1007/978-0-85729-085-4

Fildes, B. 2012. Safety benefits of automatic emergency braking systems in France, SAE Technical Paper 2012-01-0273. https://doi.org/10.4271/2012-01-0273

Fildes, B.; Keall, M.; Bos, N.; Lie, A.; Page, Y.; Pastor, C.; Pennisi, L.; Rizzi, M.; Thomas, P.; Tingvall, C. 2015. Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes, Accident Analysis & Prevention 81: 24–29. https://doi.org/10.1016/j.aap.2015.03.029

Hoogendoorn, R. G.; Tamminga, G., Hoogendoorn, S. P.; Daamen, W. 2010. Longitudinal driving behavior under adverse weather conditions: adaptation effects, model performance and freeway capacity in case of fog, in 13th International IEEE Conference on Intelligent Transportation Systems, 19–22 September 2010, Funchal, Portugal, 450–455. https://doi.org/10.1109/ITSC.2010.5625046

Kavitha, K. V. N.; Bagubali, A.; Shalini, L. 2009. V2V wireless communication protocol for rear-end collision avoidance on highways with stringent propagation delay, in 2009 International Conference on Advances in Recent Technologies in Communication and Computing, 27–28 October 2009, Kottayam, India, 661–663. https://doi.org/10.1109/ARTCom.2009.173

Kim, D.-J.; Park, K.-H.; Bien, Z. 2007. Hierarchical longitudinal controller for rear-end collision avoidance, IEEE Transactions on Industrial Electronics 54(2): 805–817. https://doi.org/10.1109/TIE.2007.891660

Li, L.; Lu G.; Wang, Y.; Tian, D. 2014. A rear-end collision avoidance system of connected vehicles, in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8–11 October 2014, Qingdao, China, 63–68. https://doi.org/10.1109/ITSC.2014.6957667

Li, X.; Luo, X.; He, M.; Chen, S. 2018. An improved car-following model considering the influence of space gap to the response, Physica A: Statistical Mechanics and its Applications 509: 536–545. https://doi.org/10.1016/j.physa.2018.06.069

Li, Y.; Zhang, L.; Song. Y. 2016. A vehicular collision warning algorithm based on the time-to-collision estimation under connected environment, in 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), 13–15 November 2016, Phuket, Thailand, 1–4. https://doi.org/10.1109/ICARCV.2016.7838789

Lv, H.; Xu, P.; Chen, H.; Zhou, B.; Ren, T.; Chen, Y. 2016. A novel rear-end collision warning algorithm in VANET, in 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), 4–6 June 2016, Beijing, China, 539–542. https://doi.org/10.1109/ICCSN.2016.7586581

Meng, F.; Gray, R.; Ho, C.; Ahtamad, M.; Spence, C. 2015. Dynamic vibrotactile signals for forward collision avoidance warning systems, Human Factors: the Journal of the Human Factors and Ergonomics Society 57(2): 329–346. https://doi.org/10.1177/0018720814542651

Nekovee, M.; Bie, J. 2013. Rear-end collision: causes and avoidance techniques, in R. Naja (Ed.). Wireless Vehicular Networks for Car Collision Avoidance, 99–119. https://doi.org/10.1007/978-1-4419-9563-6_4

Petrovai, A.; Danescu, R.-G.; Negru, M.; Vancea, C.-C.; Nedevschi, S. 2016. A stereovision based rear-end collision warning system on mobile devices, in 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP), 8–10 September 2016, Cluj-Napoca, Romania, 285–292. https://doi.org/10.1109/ICCP.2016.7737161

Rakha, H.; Pasumarthy, P.; Adjerid, S. 2009. A simplified behavioral vehicle longitudinal motion model, Transportation Letters: the International Journal of Transportation Research 1(2): 95–110. https://doi.org/10.3328/TL.2009.01.02.95-110

Rizzi, M.; Kullgren, A.; Tingvall, C. 2014. Injury crash reduction of low-speed autonomous emergency braking (AEB) on passenger cars, in 2014 IRCOBI Conference Proceedings, 10–12 September 2014, Berlin, Germany, 656–665. Available from Internet: http://www.ircobi.org/wordpress/downloads/irc14/pdf_files/73.pdf

Shah, J.; Best, M.; Benmimoun, A.; Ayat, M. L. 2015. Autonomous rear-end collision avoidance using an electric power steering system, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 229(12): 1638–1655. https://doi.org/10.1177/0954407014567517

Wächter, A.; Biegler, L. T. 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming 106(1): 25–57. https://doi.org/10.1007/s10107-004-0559-y

Wu, Y.; Abdel-Aty, M.; Park, J. 2017. Developing a rear-end crash risk algorithm under fog conditions using real-time data, in 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 26–28 June 2017, Naples, Italy, 568–573. https://doi.org/10.1109/MTITS.2017.8005736

Yang, L.; Yang, J, H.; Feron, E.; Kulkarni, V. 2003. Development of a performance-based approach for a rear-end collision warning and avoidance system for automobiles, in IEEE IV2003 Intelligent Vehicles Symposium. Proceedings, 9–11 June 2003, Columbus, OH, US, 316–321. https://doi.org/10.1109/IVS.2003.1212929

Yin, G.-D.; Chen, N.; Wang, J.-X.; Wu, L.-Y. 2011. A study on μ-synthesis control for four-wheel steering system to enhance vehicle lateral stability, Journal of Dynamic Systems, Measurement, and Control 133(1): 011002. https://doi.org/10.1115/1.4002707

Zhai, Y.; Nounou, M.; Nounou, H.; Al-Hamidi, Y. 2010. Model predictive control of a 3-DOF helicopter system using successive linearization, International Journal of Engineering, Science and Technology 2(10): 9–19. https://doi.org/10.4314/ijest.v2i10.64008