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Longitudinal Planning and Control Method for Autonomous Vehicles Based on A New Potential Field Model
Technical Paper
2017-01-1955
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
An integrated automatic driving system consists of perception, planning and control. As one of the key components of an autonomous driving system, the longitudinal planning module guides the vehicle to accelerate or decelerate automatically on the roads. A complete longitudinal planning module is supposed to consider the flexibility to various scenarios and multi-objective optimization including safety, comfort and efficiency. However, most of the current longitudinal planning methods can not meet all the requirements above. In order to satisfy the demands mentioned above, a new Potential Field (PF) based longitudinal planning method is presented in this paper. Firstly, a PF model is constructed to depict the potential risk of surrounding traffic entities, including obstacles and roads. The shape of each potential field is closely related to the property of the corresponding traffic entity. Secondly, a high-level controller and a low-level controller for the longitudinal motion are respectively designed to realize functions of the longitudinal planning and control. Based on the PF model, the longitudinal high-level controller can calculate the desired acceleration by optimizing a cost function that takes the potential risk, comfort and driving efficiency into consideration. And the longitudinal low-level controller essentially implements an adaptive PID algorithm to make the controlled vehicle follow the acceleration command well. Finally, the designed longitudinal planning and control module is integrated with a lateral planning and control module studied previously, which is also based on the same PF model. The feasibility of the proposed method in different traffic scenarios including approaching, cut-in and overtaking with multiple traffic participants is verified by co-simulation tests of CarSim/Simulink and hardware-in-the-loop tests.
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Citation
Ruan, Y., Chen, H., and Li, J., "Longitudinal Planning and Control Method for Autonomous Vehicles Based on A New Potential Field Model," SAE Technical Paper 2017-01-1955, 2017, https://doi.org/10.4271/2017-01-1955.Data Sets - Support Documents
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References
- Milakis , D. , Arem , B. , and Wee , B. Policy and society related implications of automated driving: A review of literature and directions for future research Journal of Intelligent Transportation Systems 1 25 2017 10.1080/15472450.2017.1291351
- Zhao , D. , Hu , Z. , Xia , Z. , Alippi , C. et al. Full-range adaptive cruise control based on supervised adaptive dynamic programming Neurocomputing 125 57 67 2014
- Martinez , J. and Canudas-de-Wit , C. A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios IEEE Transaction on Control Systems Technology 15 2 246 258 2007 10.1109/TCST.2006.886432
- Bacha , A. , Bauman , C. , Faruque , R. , Fleming , M. et al. Odin: Team VictorTango’s Entry in the DARPA Urban Challenge Field Robotics 25 8 467 492 2008 10.1002/rob.20248
- Urmson , C. , Anhalt , J. , Bagnell , D. , Baker , C. et al. Autonomous Driving in Urban Environments: Boss and the Urban Challenge Field Robotics 25 8 425 466 2008 10.1002/rob.20255
- Chandler , R. E. , Herman , R. , and Montroll , E. W. Traffic dynamics: studies in car following Operations Research 6 165 184 1958
- Gipps , P. G. A behavioral car following model for computer simulation Transportation Research B 15 105 111 1981
- Bando , M. , Hasebe , K. , Nakayama , A. , Shibata , A. et al. Dynamical model of traffic congestion and numerical simulation Physical Review E 51 1035 1042 1995
- Fan , X. , Li , S. and Chen , T. The robotic dynamic planning for obstacles avoidance based on a new artificial potential field function (in Chinese) Control Theory & Application 22 5 703 707 2005
- Xiu , C. and Chen , H. A Research on Local Path Planning for Autonomous Vehicle Based on Improved APF Method (in Chinese) Automotive Engineering 35 9 808 811 2013
- Li , C. , Jiang , X. , Wang , W. , Cheng , Q. et al. A Simplified Car-Following Model Based on the Artificial Potential Field Procedia Engineering 137 13 20 2016
- Leonard , N. E. , and Fiorelli , E. Virtual leaders artificial potentials and coordinated control of groups In Proceedings of the 40th IEEE, Conference on Decision and Control 2968 2973 2001
- Tu , Q. , Chen , H. , and Li , J. A Potential Field Based Lateral Planning Method for Autonomous Vehicles SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 10 1 24 34 2017 10.4271/2016-01-1874
- Christian , J. , Rossetter , E. , and Saur , U. Combining Lanekeeping and Vehicle Following with Hazard Maps Vehicle System Dynamics 36 4 5 391 411 2001 10.1076/vesd.36.4.391.3548
- Cieler , S. , Abendroth , B. , Willert , V. , Konigorski , U. et al. PRORETA3: An integrated approach to collision avoidance and vehicle automation Automatisierungstechnik 60 12 755 765 2012 10.1524/auto.2012.1046
- Tao , P. , Jin , S. , and Wang , D. A Car Following Model Based on Artificial Potential Field (in Chinese) Journal of Southeast University (Natural Science Edition) 41 4 854 858 2011 10.3969/j.issn.1001-0505.2011.04.037
- Akagi , Y. and Raksincharoensak , P. Stochastic driver speed control behavior modeling in urban intersection using risk potential-based motion planning framework IEEE Intelligent Vehicles Symposium 368 373 2015 10.1109/IVS.2015.7225713
- Wolf , M. T. and Burdick , J. W. Artificial Potential Functions for Highway Driving with Collision Avoidance IEEE International Conference on Robotics and Automation 3731 3735 2008 10.1109/ROBOT.2008.4543783
- Khatib , O. Real-Time Obstacle Avoidance for Manipulators and Mobile Robots International Journal of Robotics Research 5 1 90 98 1986
- ISO TC204/WG14 Intelligent transport systems-Full speed range adaptive cruise control (FSRA) systems-Performance requirements and test procedures ISO 22179 2009
- ISO TC204/WG14 Transport information and control systems-Adaptive cruise control systems-- Performance requirements and test procedures ISO 15622 2002
- Yi , K. , Hong , J. , and Kwon , Y. A vehicle control algorithm for stop-and-go cruise control Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 215 10 1099 1115 2001
- Zhou , L. , Bing , Y. , Li , K. , and Lian , X. Multi-layer controller design for stop and go cruise control system based on driver statistics model (in Chinese) Automotive Engineering 27 3 319 322 380 2005
- Zhan , J. An automotive longitudinal dynamic model building for adaptive cruise control (in Chinese) Journal of Jilin University (Engineering Science Edition) 36 2 157 160 2006