This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Model Predictive Automatic Lane Change Control for Intelligent Vehicles
Technical Paper
2020-01-5025
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
As a basic link of driving behavior in urban roads, vehicle lane changing has a significant impact on traffic flow characteristics and traffic safety, and the automation of lane change is also a key issue to be solved in the field of intelligent driving. In this paper, the research on the automatic lane change control for intelligent vehicles is carried out. The main work is to build the overall structure of the vehicle's automatic lane change behavior, of which the planning and tracking are focused. The strategy of Constant Time Headway (CTH) is used in the lane change decision. The lane change trajectory adopts the model of constant velocity offset plus sine function, and the longitudinal displacement is determined by the vehicle speed when changing lanes. Model Predictive Control (MPC) theory is used to track the trajectory, which optimizes tracking accuracy and vehicle stability and constrains the range and rate of change of vehicle speed and steering angle. By using weighted quadratic cost function, linearity matrix inequality constraints and upper and lower bound constraints, the multi-objective trajectory tracking problem is eventually transformed into a constrained online convex quadratic programming problem. The results of simulation and HIL test show that the scheme of automatic lane change can make the vehicle smoothly complete the lane changing behavior, and the errors can meet the error requirements of lane change. Compared with other controller, the method shows smaller lateral acceleration, stronger robustness and higher control precision during the test. Moreover, the computational time of the proposed MPC controller, implemented using the PXI, is 47.994ms during one sampling period, which can satisfy the real-time requirement.
Recommended Content
Authors
Topic
Citation
Meng, R., Guangqiang, W., Xunjie, C., and Xuyang, L., "Model Predictive Automatic Lane Change Control for Intelligent Vehicles," SAE Technical Paper 2020-01-5025, 2020, https://doi.org/10.4271/2020-01-5025.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Li , Y. , Yang , L. , and Zheng , L. Vehicle Longitudinal and Lateral Coupling Control Based on Sliding Mode Control China Mechanical Engineering 18 7 866 870 2007
- Brian , P. , Michal , C. , Yong , S.Z. et al. A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles IEEE Trans. on Intelligent Vehicles 1 1 33 55 2016 https://doi.org/10.1109/TIV.2016.2578706
- Li , W. , Gao , D. , and Duan , J. Research on Lane Change Model for Intelligent Vehicles Journal of Highway and Transportation Research and Development 27 02 119 123 2010
- Da , Y. , Shiyu , Z. , Cheng , W. et al. A Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Transportation Research Part C: Emerging Technologies 228 247 2018 https://doi.org/10.1016/j.trc.2018.06.007
- Yang , Z. , Qi , Z. , and Huang , Y. Research on Intelligent Vehicle Free Lane Trajectory Planning Journal of Chongqing Jiaotong University (Natural Science Edition) 32 03 520 524 2013
- Chee , W. and Tomizuka , M. Lane Change Maneuver of Automobiles for the Intelligent Vehicle and Highway System (IVHS) Proceedings of the 1994 American Control Conference 3 3586 3587 1994
- Shoutong , Y. , Peng , Z. , Qingyu , Z. , and Xin , H. Research on Model Predictive Control-Based Trajectory Tracking for Unmanned Vehicles 2019 4th International Conference on Control and Robotics Engineering (ICCRE) 2019 https://doi.org/10.1109/ICCRE.2019.8724158
- Luo , Y. , Xiang , Y. , Cao , K. , and Li , K. A Dynamic Automated Lane Change Maneuver Based on Vehicle-to-Vehicle Communication Transport. Res. Part C: Emerg. Technol. 62 2016 87 102 2016 https://doi.org/10.1016/j.trc.2015.11.011
- Ziegler , J. , Bender , P. , Schreiber , M. et al. Making Bertha Drive - An Autonomous Journey on a Historic Route Intelligent Transportation Systems Magazine, IEEE 6 2 8 20 2014 https://doi.org/10.1109/MITS.2014.2306552
- Zhang , H. and Wang , X. Research on Lateral Control Strategy for Automatic Lane Changing of Intelligent Vehicle Journal of Machine Design 35 4 78 83 2018 https://doi.org/10.13841/j.cnki.jxsj.2018.04.012
- Ronghui , Y. , Feng , W. , Rongben , Z. , and Wenhua , X. Lane Changing and Overtaking Control Method for Intelligent Vehicle Based on Backstepping Algorithm Transactions of the Chinese Society for Agricultural Machinery 6 011 2008
- Zhao , K. , Guo , Q. , Pei , F. , and Liang , Z. Intelligent Vehicle Trajectory Tracking Algorithm Based on Optimal Control Journal of Machinery and Electronics 36 07 76 80 2018
- GONG , J.-w. , XU , W. , JIANG , Y. et al. Multi-Constrained Model Predictive Control for Autonomous Ground Vehicle Trajectory Tracking Journal of Beijing Institute of Technology 24 04 441 448 2015 https://doi.org/10.15918/j.jbit1004-0579.201524.0403
- Nobuto , S. Hiroyuki , O. Tatsuya , S. ., et al. Simultaneous Realization of Planning and Control for Lane-Change Behavior Using Nonlinear Model Predictive Control 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018 https://doi.org/10.1109/ITSC.2018.8569973
- Wu , G. , Zhang , L. , Liu , Z. , and Guo , X. Research Status and Development Trend of Vehicle Adaptive Cruise Control Systems Journal of Tongji University (Natural Science) 45 04 544 553 2017 https://doi.org/10.11908/j.issn.0253-374x.2017.04.012
- Gong , J. , Jiang , Y. , and Xu , W. Unmanned Vehicle Model Predictive Control for Self-Driving Vehicles Beijing Beijing Institute of Technology Press 2014
- Kuhne , F. , Lages , W.F. et al. Model Predictive Control of a Mobile Robot Using Linearization Proceeding of Mechatronics and Robotics 4 525 530 2004
- Zhang , J.-H. , Li , Q. , and Chen , D.-P. Drivers Imitated Multi-Objective Adaptive Cruise Control Algorithm Journal of Control Theory and Applications 35 6 140 147 2018 https://doi.org/10.7641/CTA.2017.70585