Abstract
This paper presents a model predictive trajectory optimization and tracking framework to avoid collisions for autonomous vehicles in highly constrained environments. Firstly, a vehicle model is established in road coordinate system to describe the relationship between the vehicle and the reference road. Secondly, a numerical optimization method is applied to smoothen the reference path generated by waypoints. Then, a multilayer searched method is used to establish a safe driving corridor in highly constrained environments. In addition, an optimal path optimization and tracking framework based on model predictive control is formulated to improve the driving safety and comfort. The proposed framework considers the constraints of path boundaries and vehicle dynamics to provide the optimal control command. Furthermore, the speed profile is optimized based on the longitudinal motion model in space domain to ensure the constraints of speed limits and vehicle acceleration. Finally, the proposed algorithms are evaluated through experiments in various scenarios to demonstrate the effectiveness.
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References
Chu, K., Lee, M. and Sunwoo, M. (2012). Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans. Intelligent Transportation Systems 13, 4, 1599–1616.
Fassbender, D., Heinrich, B. C. and Wuensche, H. J. (2016). Motion planning for autonomous vehicles in highly constrained urban environments. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Daejeon, Korea.
Gutjahr, B., Gröll, L. and Werling, M. (2016). Lateral vehicle trajectory optimization using constrained linear time-varying MPC. IEEE Trans. Intelligent Transportation Systems 18, 6, 1586–1595.
Howard, T. M., Green, C. J., Kelly, A. and Ferguson, D. (2008). State space sampling of feasible motions for high-performance mobile robot navigation in complex environments. J. Field Robotics 25, 6–7, 325–345.
Huang, Y., Ding, H., Zhang, Y., Wang, H., Cao, D., Xu, N. and Hu, C. (2019). A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach. IEEE Trans. Industrial Electronics 67, 2, 1376–1386.
Ji, J., Khajepour, A., Melek, W. W. and Huang, Y. (2016). Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE Trans. Vehicular Technology 66, 2, 952–964.
Katrakazas, C., Quddus, M., Chen, W. H. and 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, 416–142.
Li, H. and Luo, Y. (2020). Integrated coordination control for distributed drive electric vehicle trajectory tracking. Int. J. Automotive Technology 21, 4, 1047–1060.
Li, X., Sun, Z., Liu, D., Zhu, Q. and Huang, Z. (2014). Combining local trajectory planning and tracking control for autonomous ground vehicles navigating along a reference path. 17th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Qingdao, China.
Plessen, M. G. (2017). Trajectory planning of automated vehicles in tube-like road segments. 20th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Yokohama, Japan.
Qian, X., Altché, F., Bender, P., Stiller, C. and de La Fortelle, A. (2016). Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective. 19th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.
Stellato, B., Banjac, G., Goulart, P., Bemporad, A. and Boyd, S. (2020). OSQP: An operator splitting solver for quadratic programs. Mathematical Programming Computation 12, 4, 637–672.
Wächter, A. and 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.
Wang, Z., Li, G., Jiang, H., Chen, Q. and Zhang, H. (2018). Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control. IEEE/ASME Trans. Mechatronics 23, 3, 1103–1113.
Zhang, S., Chen, Y., Chen, S. and Zheng, N. (2019). Hybrid A*-based curvature continuous path planning in complex dynamic environments. IEEE Intelligent Transportation Systems Conf. (ITSC), Auckland, New Zealand.
Zhang, Y., Chen, H., Waslander, S. L., Gong, J., Xiong, G., Yang, T. and Liu, K. (2018). Hybrid trajectory planning for autonomous driving in highly constrained environments. IEEE Access, 6, 32800–32819.
Acknowledgement
This work was supported by the National Key R&D Program of China under Grant No. 2021YFB2501201, the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100 and the National Natural Science Foundation of China under Grant 52002284.
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Fu, Z., Xiong, L., Qian, Z. et al. Model Predictive Trajectory Optimization and Tracking in Highly Constrained Environments. Int.J Automot. Technol. 23, 927–938 (2022). https://doi.org/10.1007/s12239-022-0081-3
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DOI: https://doi.org/10.1007/s12239-022-0081-3