Home / Regular Issue / JST Vol. 29 (1) Jan. 2021 / JST-2055-2020


A New Parametric Function-Based Dynamic Lane Changing Trajectory Planning and Simulation Model

Md. Mijanoor Rahman, Mohd. Tahir Ismail, Norhashidah Awang and Majid Khan Majahar Ali

Pertanika Journal of Science & Technology, Volume 29, Issue 1, January 2021

DOI: https://doi.org/10.47836/pjst.29.1.12

Published: 22 January 2021

Lane-changing (LC) problem may cause serious accidents or create a painful traffic jam at multi-lane roads. Existing LC simulation model was created with some limitations (less fitted, without velocity and acceleration profiles, high curvature) by using well known trajectory curve such as Hyperbolic Tangent Curve (HTC), Sine-Based Curve (SC), Polynomial Curve (PC). In this study, a new parametric curve had been proposed by using curvilinear coordinate system and fitted against Next Generation Simulation (NGSIM) real dataset. Further, new profiles of velocity and acceleration were designed using the proposed LC trajectory curve. The curvature of proposed model was zero-based curvature both at LC starting and ending points. This proposed curvature was compared with two models such as HTC and SC. The average root-mean-square-error of proposed model decreased with 1.84% for left LC and 15.48% for right LC compared to HTC model and 1.74% for left LC and 15.60% for right LC compared to SC model. Similarly, the proposed model for velocity and acceleration profiles improved significantly from PC model. The proposed parametric curve solves the gap and collision points of LC vehicle with a front vehicle and rear vehicle at target lane and can be used in real LC path planning.

  • Ali, Y., Zheng, Z., Haque, M., & Wang, M. (2019). A game theory-based approach for modelling mandatory lane- changing behaviour in a connected environment. Transportation Research Part C, 106(February), 220-242. doi: https://doi.org/10.1016/j.trc.2019.07.011

  • Chebly, A., Talj, R., & Charara, A. (2017, October 16-19). Maneuver planning for autonomous vehicles , with clothoid tentacles for local trajectory planning. In 20th IEEE International Conference on Intelligent Trans- Portation (ITSC 2017) (pp. 1-6). Yokohama, Japan. doi: 10.1109/ITSC.2017.8317856

  • Connors, J., & Elkaim, G. (2007, April 22-25). Analysis of a spline based, obstacle avoiding path planning algorithm. In IEEE Vehicular Technology Conference (pp. 2565-2569). Dublin, Ireland. doi: https://doi.org/10.1109/VETECS.2007.528

  • Dong, C., Zhang, Y., & Dolan, J. M. (2017, September 24-28). Lane-change social behavior generator for autonomous driving car by non-parametric regression in reproducing kernel hilbert space. In IEEE International Conference on Intelligent Robots and Systems, 2017-Septe (pp. 4489-4494). Vancouver, BC, Canada. doi: https://doi.org/10.1109/IROS.2017.8206316

  • González, D., Pérez, J., Milanés, V., & Nashashibi, F. (2016). A review of motion planning techniques for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1135-1145. doi: https://doi.org/10.1109/TITS.2015.2498841

  • Gu, X., Yu, J., Han, Y., Han, M., & Wei, L. (2019, July 12-14). Vehicle lane change decision model based on random forest. In 2019 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2019 (pp. 115-120). Shenyang, China. doi: https://doi.org/10.1109/ICPICS47731.2019.8942520

  • Heil, T., Lange, A., & Cramer, S. (2016, November 1-4). Adaptive and efficient lane change path planning for automated vehicles. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (pp. 479-484). Rio de Janeiro, Brazil. doi: https://doi.org/10.1109/ITSC.2016.7795598

  • Katrakazas, C., Quddus, M., Chen, W. H., & Deka, L. (2015). Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies, 60, 416-442. doi: https://doi.org/10.1016/j.trc.2015.09.011

  • Kawabata, K., Ma, L., Xue, J., & Zheng, N. (2013, July 9-12). A path generation method for automated vehicles based on Bezier curve. In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013 (pp. 991-996). Wollongong, NSW, Australia. doi: https://doi.org/10.1109/AIM.2013.6584223

  • Léger, J. C. (1999). Menger curvature and rectifiability. Annals of Mathematics, 149(3), 831-869. doi: https://doi.org/10.2307/121074

  • Li, L., Chen, X. M., & Zhang, L. (2016). A global optimization algorithm for trajectory data based car-following model calibration. Transportation Research Part C: Emerging Technologies, 68, 311-332. doi: https://doi.org/10.1016/j.trc.2016.04.011

  • Ntousakis, I. A., Nikolos, I. K., & Papageorgiou, M. (2016). Optimal vehicle trajectory planning in the context of cooperative merging on highways. Transportation Research Part C, 71, 464-488. doi: https://doi.org/10.1016/j.trc.2016.08.007

  • Resende, P., & Nashashibi, F. (2010, September 19-22). Real-time dynamic trajectory planning for highly automated driving in highways. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 653-658). Funchal, Portugal. doi: https://doi.org/10.1109/ITSC.2010.5625194

  • Sanchez-Reyes, J., & Chacón, J. M. (2018). Representation of polynomial nonparametric B e transition curves. Journal of Surveying Engineering, 144(2), 1-8. doi: https://doi.org/10.1061/(ASCE)SU.1943-5428.0000251

  • Shen, P., Zhang, X., & Fang, Y. (2017). Essential properties of numerical integration for time-optimal path-constrained trajectory planning. IEEE Robotics and Automation Letters, 2(2), 888-895. doi: https://doi.org/10.1109/LRA.2017.2655580

  • Thiemann, C., Treiber, M., & Kesting, A. (2008). Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data. Transportation Research Record, 2088(1), 90-101. https://doi.org/10.3141/2088-10

  • Wan, Q., Peng, G., Li, Z., Hiroshi, F., & Inomata, T. (2020). Spatiotemporal trajectory characteristic analysis for traffic state transition prediction near expressway merge bottleneck. Transportation Research Part C, 117, 1-24. doi: https://doi.org/10.1016/j.trc.2020.102682

  • Wang, C., & Zheng, C. Q. (2013). Lane change trajectory planning and simulation for intelligent vehicle. In Y. Huang, T. Bao & H. Wang (Eds.), Advanced materials research (Vol. 671-674, pp. 2843-2846). New York, USA: Trans Tech Publications Ltd. doi: https://doi.org/10.4028/www.scientific.net/AMR.671-674.2843

  • Wang, Y. Y., Pan, D., Liu, Z., & Feng, R. (2018). Study on lane change trajectory planning considering of driver characteristics (No. 2018-01-1627). SAE Technical Papers. doi: https://doi.org/10.4271/2018-01-1627

  • Yang, D., Zheng, S., Wen, C., Jin, P. J., & Ran, B. (2018). A dynamic lane-changing trajectory planning model for automated vehicles. Transportation Research Part C: Emerging Technologies, 95(June), 228-247. doi: https://doi.org/10.1016/j.trc.2018.06.007

  • You, F., Zhang, R., Lie, G., Wang, H., Wen, H., & Xu, J. (2015). Expert systems with applications trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Systems With Applications, 42(14), 5932-5946. doi: https://doi.org/10.1016/j.eswa.2015.03.022

  • Zhou, B., Wang, Y., Yu, G., & Wu, X. (2017). A lane-change trajectory model from drivers’ vision view. Transportation Research Part C: Emerging Technologies, 85(October), 609-627. doi: https://doi.org/10.1016/j.trc.2017.10.013