A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles

WANG Huixian; LI Bo; ZHENG Hongjiang; CHEN Wei

doi:10.3963/j.jssn.1674-4861.2021.01.009

Volume 39 Issue 1

Feb. 2021

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Article Navigation > Journal of Transport Information and Safety > 2021 > 39(1): 76-86

WANG Huixian, LI Bo, ZHENG Hongjiang, CHEN Wei. A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 76-86. doi: 10.3963/j.jssn.1674-4861.2021.01.009

Citation:

WANG Huixian, LI Bo, ZHENG Hongjiang, CHEN Wei. A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 76-86. doi: 10.3963/j.jssn.1674-4861.2021.01.009

Citation:

WANG Huixian, LI Bo, ZHENG Hongjiang, CHEN Wei. A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles[J]. Journal of Transport Information and Safety, 2021, 39(1): 76-86. doi: 10.3963/j.jssn.1674-4861.2021.01.009

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A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles

doi: 10.3963/j.jssn.1674-4861.2021.01.009

WANG Huixian^1
,,
LI Bo^{2, 3},
ZHENG Hongjiang^{2, 3},
CHEN Wei^{4
,
,}

1.
School of Information Management, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China
2.
Department of Technology, Shanghai Engineering Technology Research Center for Intelligent and Connected Vehicle Terminals, Shanghai 200030, China
3.
Department of Technology, Shanghai PATEO Electronic Equipment Manufacturing Limited Company, Shanghai 200030, China
4.
School of Automation, Wuhan University of Technology, Wuhan 430070, China

Received Date: 2020-07-23
Publish Date: 2021-02-28

Abstract

Abstract

Traditional model-based control methods need to obtain parameters of driving behaviors of drivers and system dynamics of vehicles in a multi-vehicle cooperative control system. However, these parameters cannot be obtained accurately in actual transport systems. A data-driven adaptive dynamic programming control algorithm is proposed to solve the problem. Under the environment of mixed manned and unmanned vehicles, the horizontal and vertical control models of the multi-vehicle cooperative control system are analyzed to derive its state equation. A recursive numerical method is used to approximate an optimal solution. Optimal control inputs are obtained by optimizing a feedback control matrix. The proposed algorithm simplifies control input parameters of the system. Besides, the optimal control of unmanned vehicles can be realized only using two parameters of basic safety messages of vehicles in real-time as the controller's inputs: steering the angel of the fore wheel and expected longitudinal acceleration. A co-simulation is conducted based on CarSim and Simulink. The results show that the proposed algorithm has simple control parameters, fast convergence speed, high control accuracy, and strong adaptability. It ensures the stability of the multi-vehicle cooperative control system and controls unmanned vehicles in platooning to maintain the desired velocity and desired heading. Moreover, its lateral error between the actual trajectory and expected trajectory tends to zero during driving on the road with arbitrary curvature.
- intelligent transportation,
- multi-vehicle collaboration control,
- data-driven control,
- adaptive dynamic pro-gramming,
- V2X communication,
- human-driven and autonomous vehicles

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[1]	ERSAL T, KOLMANOVSKY I, MASOUD N, et al. Connected and automated road vehicles: State of the art and future challenges[J]. Vehicle System Dynamics, 2020, 58(5): 672-704. doi: 10.1080/00423114.2020.1741652
[2]	DEY K C, YAN L, WANG X, et al. A review of communication, driver characteristics, and controls aspects of cooperative adaptive cruise control(CACC)[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 491-509. doi: 10.1109/TITS.2015.2483063
[3]	XIAO L, GAO F. Practical string stability of platoon of adaptive cruise control vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1184-1194. doi: 10.1109/TITS.2011.2143407
[4]	韩璐. 基于鲁棒自适应控制的车辆领航编队控制方法研究[D]. 北京: 北京交通大学, 2015. HAN Lu. Research on vehicle formation control based on robust adaptive control algorithm[D]. Beijing: Beijing Jiaotong University, 2015. (in Chinese)
[5]	DING J, PENG H, ZHANG Y, et al. Penetration effect of connected and automated vehicles on cooperative on-ramp merging[J]. IET Intelligent Transport Systems, 2020, 14(1): 56-64. doi: 10.1049/iet-its.2019.0488
[6]	LI Yongfu, ZHANG Li, ZHENG Hong, et al. Evaluating the energy consumption of electric vehicles based on car-following model under non-lane discipline[J]. Nonlinear Dynamics, 2015(82): 629-641. http://smartsearch.nstl.gov.cn/paper_detail.html?id=7e0e18c866cda534db079396ed2e94f3
[7]	KIM S G, TOMIZUKA M, CHENG K H. Smooth motion control of the adaptive cruise control system by a virtual lead vehicle[J]. International Journal of Automotive Technology, 2012, 13(1): 77-85. doi: 10.1007/s12239-012-0007-6
[8]	MILANES V, SHLADOVER S E, SPRING J, et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 296-305. doi: 10.1109/TITS.2013.2278494
[9]	ONCU S, PLOEG J, VAN D W N, et al. Cooperative adaptive cruise control: Network-aware analysis of string stability[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4): 1527-1537. doi: 10.1109/TITS.2014.2302816
[10]	PLOEG J, SCHEEPERS B T M, NUNEN V, et al. Design and experimental evaluation of cooperative adaptive cruise control[C]. 14^th International IEEE Conference on Intelligent Transportation Systems (ITSC). Washington, DC, USA: IEEE, 2011.
[11]	ENGLUND C, CHEN L, PLOEG J, et al. The grand cooperative driving challenge 2016: Boosting the introduction of cooperative automated vehicles[J]. IEEE Wireless Communications, 2016, 23(4): 146-152. doi: 10.1109/MWC.2016.7553038
[12]	GE J I, OROSZ G. Optimal control of connected vehicle systems[C]. 53^rd IEEE Conference on Decision and Control. Los Angeles, California, USA: IEEE, 2014.
[13]	STANGER T, RE L D. A model predictive cooperative adaptive cruise control approach[C]. American Control Conference, Washington, D.C. USA: IEEE, 2013
[14]	CAO W J, MUKAI M, KAWABE T, et al. Cooperative vehicle path generation during merging using model predictive control with real-time optimization[J]. Control Engineering Practice, 2015(34): 98-105. http://www.sciencedirect.com/science/article/pii/S0967066114002408
[15]	DESJARDINS C, CHAIB-DRAA B. Cooperative adaptive cruise control: A reinforcement learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12 (4): 1248-1260. doi: 10.1109/TITS.2011.2157145
[16]	MASATO ABE. Vehicle handling dynamics: theory and application[M]. India: Butterworth-Heinemann Illustration, 2015.
[17]	GAO W, JIANG Z P, OZBAY K. Data-driven adaptive optimal control of connected vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(5): 1122-1133. http://ieeexplore.ieee.org/document/7219749
[18]	BERTSEKAS D P. Dynamic programming and optimal control. Vol. 1, 4^th Ed. Belmont, MA: Athena scientific, 2017.
[19]	LIU D, YU W W, BALDI S, et al. A switching-based adaptive dynamic programming method to optimal traffic signaling[J]. IEEE Transactions on Systems, 2020, 50(11): 4160-4170. http://www.researchgate.net/publication/335007289_A_Switching-Based_Adaptive_Dynamic_Programming_Method_to_Optimal_Traffic_Signaling

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A Multi-vehicle Cooperative Control Algorithm Based on Data-Driven Adaptive Control Strategy for Heterogeneous Human-driven and Autonomous Vehicles

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