Abstract
This paper presents a trajectory planning framework to deal with the highly dynamic environments for on-road driving. The trajectory optimization problem with parameterized curvature control was formulated to reach the goal state with the vehicle model and its dynamic constraints considered. This in contrast to existing curve fitting techniques guarantees the dynamic feasibility of the planned trajectory. With generation of multiple trajectory candidates along the Frenet frame, the vehicle is reactive to other road users or obstacles encountered. Additionally, to deal with more complex driving scenarios, its seamless interaction with an upper behavior planning layer was considered by having longitudinal motion planning responsive to the desired goal state. The trajectory evaluation and selection methodologies, along with the low-level tracking control, were also developed under this framework. The potential of the proposed trajectory planning framework was demonstrated under different dynamic driving scenarios such as lane-changing or merging with surrounding vehicles with its computation efficiency proven in real-time simulations.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Gonzalez, D., Perez, J., Milanes, V., Nashashibi, F.: A review of motion planning techniques for automated vehicles. IEEE Trans. Intell. Transp. Syst. 17(4), 1135–1145 (2016). https://doi.org/10.1109/tits.2015.2498841
Ma, L., Xue, J., Kawabata, K., Zhu, J., Ma, C., Zheng, N.: Efficient sampling-based motion planning for on-road autonomous driving. IEEE Trans. Intell. Transp. Syst. 16(4), 1961–1976 (2015). https://doi.org/10.1109/tits.2015.2389215
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011). https://doi.org/10.1177/0278364911406761
Yoon, S., Lee, D., Jung, J., Shim, D.H.: Spline-based RRT∗ using piecewise continuous collision-checking algorithm for Car-like vehicles. J. Intell. Robot. Syst. 90(3–4), 537–549 (2017). https://doi.org/10.1007/s10846-017-0693-4
Kato, S., Takeuchi, E., Ishiguro, Y., Ninomiya, Y., Takeda, K., Hamada, T.J.I.M.: An open approach to autonomous vehicles. 35(6), 60–68 (2015)
Zhang, C., Chu, D., Liu, S., Deng, Z., Wu, C., Su, X.: Trajectory planning and tracking for autonomous vehicle based on state lattice and model predictive control. IEEE Intell. Transp. Syst. Mag. 11(2), 29–40 (2019). https://doi.org/10.1109/mits.2019.2903536
Dolgov, D., Thrun, S.: Autonomous driving in semi-structured environments: mapping and planning. In: 2009 IEEE international conference on robotics and automation 2009, pp. 3407-3414. IEEE
Sedighi, S., Nguyen, D.-V., Kuhnert, K.-D.: Guided hybrid A-star path planning algorithm for valet parking applications. In: 2019 5th international conference on control, automation and robotics (ICCAR) 2019, pp. 570-575. IEEE
Ji, J., Khajepour, A., Melek, W.W., Huang, Y.: Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans. Veh. Technol. 66(2), 952–964 (2017). https://doi.org/10.1109/tvt.2016.2555853
Montiel, O., Orozco-Rosas, U., Sepúlveda, R.: Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst. Appl. 42(12), 5177–5191 (2015). https://doi.org/10.1016/j.eswa.2015.02.033
Guo, H., Shen, C., Zhang, H., Chen, H., Jia, R.: Simultaneous trajectory planning and tracking using an MPC method for cyber-physical systems: a case study of obstacle avoidance for an intelligent vehicle. IEEE Trans. Ind. Inform. 14(9), 4273–4283 (2018). https://doi.org/10.1109/tii.2018.2815531
Yi, B., Gottschling, S., Ferdinand, J., Simm, N., Bonarens, F., Stiller, C.: Real time integrated vehicle dynamics control and trajectory planning with MPC for critical maneuvers. In: 2016 IEEE intelligent vehicles symposium (IV) 2016, pp. 584-589. IEEE
Ziegler, J., Bender, P., Dang, T., Stiller, C.: Trajectory planning for Bertha—a local, continuous method. In: 2014 IEEE intelligent vehicles symposium proceedings 2014, pp. 450-457. IEEE
Gelbal, S.Y., Aksun-Guvenc, B., Guvenc, L.: Elastic Band Collision Avoidance of Low Speed Autonomous Shuttles with Pedestrians. International Journal of Automotive Technology, accepted (2020)
Do, Q.H., Han, L., Nejad, H.T.N., Mita, S.: Safe path planning among multi obstacles. In: 2011 IEEE intelligent vehicles symposium (IV) 2011, pp. 332-338. IEEE
Osman, K., Ghommam, J., Saad, M.: Guidance based lane-changing control in high-speed vehicle for the overtaking maneuver. J. Intell. Robot. Syst. (2019). https://doi.org/10.1007/s10846-019-01070-6
You, F., Zhang, R., Lie, G., Wang, H., Wen, H., Xu, J.: Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Syst. Appl. 42(14), 5932–5946 (2015). https://doi.org/10.1016/j.eswa.2015.03.022
Ntousakis, I.A., Nikolos, I.K., Papageorgiou, M.: Optimal vehicle trajectory planning in the context of cooperative merging on highways. Trans. Res. Part C: Emerg. Technol. 71, 464–488 (2016). https://doi.org/10.1016/j.trc.2016.08.007
Werling, M., Kammel, S., Ziegler, J., Gröll, L.: Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. Int. J. Robot. Res. 31(3), 346–359 (2011). https://doi.org/10.1177/0278364911423042
Hu, X., Chen, L., Tang, B., Cao, D., He, H.: Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles. Mech. Syst. Signal Process. 100, 482–500 (2018). https://doi.org/10.1016/j.ymssp.2017.07.019
Ma, L., Yang, J., Zhang, M.: A two-level path planning method for on-road autonomous driving. In: 2012 second international conference on intelligent system design and engineering application 2012, pp. 661-664. IEEE
Nagasaka, N., Harada, M.: Towards safe, smooth, and stable path planning for on-road autonomous driving under uncertainty. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC) 2016, pp. 795-801. IEEE
Howard, T.M., Kelly, A.: Optimal rough terrain trajectory generation for wheeled Mobile robots. Int. J. Robot. Res. 26(2), 141–166 (2007). https://doi.org/10.1177/0278364906075328
Li, X., Sun, Z., Cao, D., Liu, D., He, H.: Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mech. Syst. Signal Process. 87, 118–137 (2017). https://doi.org/10.1016/j.ymssp.2015.10.021
Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M.N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., Gittleman, M., Harbaugh, S., Hebert, M., Howard, T.M., Kolski, S., Kelly, A., Likhachev, M., McNaughton, M., Miller, N., Peterson, K., Pilnick, B., Rajkumar, R., Rybski, P., Salesky, B., Seo, Y.-W., Singh, S., Snider, J., Stentz, A., Whittaker, W.R., Wolkowicki, Z., Ziglar, J., Bae, H., Brown, T., Demitrish, D., Litkouhi, B., Nickolaou, J., Sadekar, V., Zhang, W., Struble, J., Taylor, M., Darms, M., Ferguson, D.: Autonomous driving in urban environments: Boss and the Urban Challenge. J. Field Robot. 25(8), 425–466 (2008). https://doi.org/10.1002/rob.20255
Altché, F., Polack, P., de La Fortelle, A.: High-speed trajectory planning for autonomous vehicles using a simple dynamic model. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC) 2017, pp. 1-7. IEEE
Kelly, A., Nagy, B.: Reactive Nonholonomic trajectory generation via parametric optimal control. Int. J. Robot. Res. 22(7–8), 583–601 (2003). https://doi.org/10.1177/02783649030227008
Nocedal, J., Wright, S.: Numerical optimization. Springer Science & Business Media, (2006)
Do, Q.H., Tehrani, H., Mita, S., Egawa, M., Muto, K., Yoneda, K.J.I.I.T.S.M.: Human drivers based active-passive model for automated lane change. 9(1), 42–56 (2017)
Zhu, S., Gelbal, S.Y., Aksun-Guvenc, B., Guvenc, L.: Parameter-space based robust gain-scheduling Design of Automated Vehicle Lateral Control. IEEE Trans. Veh. Technol. 68(10), 9660–9671 (2019). https://doi.org/10.1109/tvt.2019.2937562
Talcahashi, A., Hongo, T., Ninomiya, Y., Sugimoto, G.: Local Path Planning and Motion Control for Agv in Positioning. (1989)
Ziegler, J., Stiller, C.: Fast collision checking for intelligent vehicle motion planning. In: 2010 IEEE intelligent vehicles symposium 2010, pp. 518-522. IEEE
Lim, W., Lee, S., Sunwoo, M., Jo, K.: Hierarchical trajectory planning of an autonomous Car based on the integration of a sampling and an optimization method. IEEE Trans. Intell. Transp. Syst. 19(2), 613–626 (2018). https://doi.org/10.1109/tits.2017.2756099
Sharp, R.S.: Driver steering control and a new perspective on Car handling qualities. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 219(10), 1041–1051 (2005). https://doi.org/10.1243/095440605x31896
Zhu, S., Gelbal, S.Y., Li, X., Cantas, M.R., Aksun-Guvenc, B., Guvenc, L.: Parameter space and model regulation based robust, scalable and replicable lateral control Design for Autonomous Vehicles. In: 2018 IEEE conference on decision and control (CDC) 2018, pp. 6963-6969. IEEE
Acknowledgements
The author would like to thank the Smart Campus organization of the Ohio State University and Smart Columbus for partial support of the work presented here.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhu, S., Aksun-Guvenc, B. Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments. J Intell Robot Syst 100, 1055–1067 (2020). https://doi.org/10.1007/s10846-020-01215-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-020-01215-y