Driver Control Behavior Model for Tracked Vehicle with Stepped Steering Mechanism

doi:10.3969/j.issn.1000-1093.2020.12.002

Abstract

Abstract: For the representation of driving skills at the control level of tracked vehicle with stepped steering mechanism, a driver control behavior model based on control primitive sequence is proposed by utilizing a large amount of collected real driving data. The control primitives are characterized by Gaussian mixture models, and the digraph is applied to complete the extraction and category identification of the driver control primitives sequence. According to the recognition results of different types of control primitive sequence, the driving data is regrouped, and the hidden Markov model-Gaussian mixture model is used to train the driver control behavior model for each type of sequence. The results show that the extracted control primitive sequence can not only represent the driver's steering control primitives switching behavior, but also realize the reasonable division of the trajectory primitive types. Within the predicted time domain corresponding to a given desired trajectory, the proposed model can predict driver control behavior with an average deviation of 4.2% under certain conditions.

Key words: trackedvehicle, drivermodel, controlprimitivesequence, hiddenMarkovmodel-Gaussianmixturemodel

CLC Number:

TJ810.2

WANG Boyang, GONG Jianwei, XIONG Guangming, ZHANG Ruizeng, CHEN Huiyan, XI Junqiang. Driver Control Behavior Model for Tracked Vehicle with Stepped Steering Mechanism[J]. Acta Armamentarii, 2020, 41(12): 2379-2388.

References

［1］闫清东，张连第，赵毓芹，等. 坦克构造与设计[M]. 北京：北京理工大学出版社，2007.
YAN Q D, ZHANG L D, ZHAO Y Q, et al. Structure and design of tank[M]. Beijing: Beijing Institute of Technology Press, 2007.(in Chinese)
[2］王博洋，龚建伟，高天云,等. 基于高斯混合-隐马尔可夫模型的速差转向履带车辆横向控制驾驶员模型[J]. 兵工学报, 2017, 38(12): 2301-2308.
WANG B Y, GONG J W, GAO T Y, et al. Steering control driver model of skid steering vehicle based on Gaussian mixture model-hidden Markov model[J]. Acta Armamentarii, 2017, 38(12): 2301-2308. (in Chinese)
[3］王博洋, 龚建伟, 高天云,等. 基于双层驾驶员模型的履带车辆纵向与横向协同跟踪控制方法[J]. 兵工学报, 2018, 39(9): 1675-1682.
WANG B Y, GONG J W, GAO T Y, et al. Longitudinal and la-teral path following coordinate control method of tracked vehicle based on double-layer driver model[J]. Acta Armamentarii, 2018, 39(9): 1675-1682. (in Chinese)
[4］胡家铭, 胡宇辉, 陈慧岩, 等. 基于模型预测控制的无人驾驶履带车辆轨迹跟踪方法研究[J]. 兵工学报, 2019, 40(3): 456-463.
HU J M, HU Y H, CHEN H Y, et al. Research on trajectory tracking of unmanned tracked vehicles based on model predictive control[J]. Acta Armamentarii, 2019, 40(3): 456-463. (in Chinese)
[5］ ARGALL B D, CHERNOVA S, VELOSO M, et al. A survey of robot learning from demonstration[J]. Robotics and Autonomous Systems, 2009, 57(5): 469-483.
[6］ WANG B, LI Z, GONG J, et al. Learning and generalizing motion primitives from driving data for path-tracking applications[C]∥Proceedings of 2018 IEEE Intelligent Vehicles Symposium. Changshu, China: IEEE, 2018: 1191-1196.
[7］ WANG B, GONG J, ZHANG R, et al. Learning to segment and represent motion primitives from driving data for motion planning applications[C]∥Proceedings of the 21st International Conference on Intelligent Transportation Systems. Maui, HI, US: IEEE, 2018: 1408-1414.
[8］ KULIC D, OTT C, LEE D, et al. Incremental learning of full body motion primitives and their sequencing through human motion observation[J]. The International Journal of Robotics Research, 2012, 31(3): 330-345.
[9］ MANSCHITZ S, KOBER J, GIENGER M, et al. Learning to sequence movement primitives from demonstrations[C]∥Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, US :IEEE, 2014: 4414-4421.
[10］胡云峰, 曲婷, 刘俊, 等. 智能汽车人机协同控制的研究现状与展望[J]. 自动化学报, 2019, 45(7): 1261-1280.
HU Y F, QU T, LIU J, et al. Human-machine cooperative control of intelligent vehicle: recent developments and future perspectives[J]. Acta Automatica Sinica, 2019, 45(7): 1261-1280.(in Chinese)
[11］ KAPLAN S, GUVENSAN M A, YAVUZ A G, et al. Driver behavior analysis for safe driving: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3017-3032.
[12］ YI D, SU J, LIU C, et al. Trajectory clustering aided persona-lized driver intention prediction for intelligent vehicles[J]. IEEE Transactions on Industrial Informatics, 2019,15(6):3693-3702.
[13］ TANIGUCHI T, NAGASAKA S, HITORNI K, et al. Sequence prediction of driving behavior using double articulation analyzer[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(9): 1300-1313.
[14］ HAMADA R, KUBO T, IKEDA K, et al. Modeling and prediction of driving behaviors using a nonparametric Bayesian method with AR models[J]. IEEE Transactions on Intelligent Vehicles, 2016, 1(2): 131-138.

[1]	ZHANG Wei， LIU Hui， HAN Lijin， LIU Baoshuai， ZHANG Xun， ZHANG Wannian. Intelligent Control Strategy of Electromechanical Braking for Hybrid Tracked Vehicle [J]. Acta Armamentarii, 2022, 43(5): 969-981.
[2]	WANG Boyang, GUAN Haijie, GONG Jianwei, CHEN Huiyan, ZHAO Huijing. Unified Motion Planning Method for Heterogeneous Tracked Vehicles [J]. Acta Armamentarii, 2022, 43(2): 241-251.
[3]	XU Baorong, WANG Tao, LIANG Zi. Generating Method and Process of Load Spectra for Tracked Vehicles Based on Prediction of Manipulating Action [J]. Acta Armamentarii, 2022, 43(2): 252-259.
[4]	LI Rui， XIANG Changle， WANG Chao， FAN Jingjing， LIU Chunlin. Robust Adaptive Trajectory Tracking Control Approach for Autonomous Tracked Vehicles [J]. Acta Armamentarii, 2021, 42(6): 1128-1137.
[5]	ZHANG Jie, MA Xiaojun, LIU Chunguang, YUAN Dong, ZHANG Yunyin. Feedback Linearization Decoupling and Predictive Driving Control for Dual Independent Electric Drive Tracked Vehicle [J]. Acta Armamentarii, 2021, 42(4): 697-705.
[6]	LI Chunming, WU Wei, GUO Zhiqiang, YUAN Shihua, CHEN Si. Longitudinal and Vertical Coupled Dynamic Model and Power Characteristics of Tracked Vehicle [J]. Acta Armamentarii, 2021, 42(3): 449-458.
[7]	ZHAO Yidong, CHEN Huiyan, HU Jiaming. Control Method for AMT Shifting of a Shift-by-wire Bilateral Electric Drive Tracked Vehicle [J]. Acta Armamentarii, 2021, 42(3): 459-467.
[8]	GAI Jiangtao， LIU Chunsheng， MA Changjun， SHEN Hongji. Steering Control of Electric Drive Tracked Vehicle Considering Tracks' Skid and Slip [J]. Acta Armamentarii, 2021, 42(10): 2092-2101.
[9]	ZOU Yuan, ZHANG Bin, ZHANG Xudong, ZHAO Zhiying, KANG Tieyu, GUO Yufeng, WU Zhe. Energy Management of Hybrid Tracked Vehicle Based on Reinforcement Learning with Normalized Advantage Function [J]. Acta Armamentarii, 2021, 42(10): 2159-2169.
[10]	MA Tian， WANG Zhitao, ZHANG Xin， PANG Daqian， GAO Xiaoyu. On Thermal Property of Reduction Gear for Electrical Transmission System of Tracked Vehicle [J]. Acta Armamentarii, 2021, 42(10): 2170-2179.
[11]	GAI Jiangtao， SHENG Hui， ZHOU Guangming， LIU Lifang. Power Balance Control Strategy of Series Hybrid Tracked Vehicle under Rapid Acceleration [J]. Acta Armamentarii, 2021, 42(10): 2180-2188.
[12]	ZENG Gen， WANG Weida， GAI Jiangtao， MA Changjun， LI Xunming， LI Huanhuan. Steering on Ramp Control Strategy of Double Motor Coupling Drive Transmission for Tracked Vehicle [J]. Acta Armamentarii, 2021, 42(10): 2189-2195.
[13]	ZOU Tiangang， YAN Qingdong， GAI Jiangtao， HOU Wei， WANG Zhitao， SHUAI Zhibin， SUN Xueyan. The Scheme of Lightweight Integrated Mixing Transmission Based on Flat Motor for Tracked Vehicle [J]. Acta Armamentarii, 2021, 42(10): 2233-2241.
[14]	ZHANG Ruizeng, GONG Jianwei, CHEN Huiyan, WANG Boyang, LIU Haiou. Hill-start Control of a Medium Unmanned Tracked Vehicle with Single Circulating Moving [J]. Acta Armamentarii, 2020, 41(8): 1590-1599.
[15]	ZHANG Jie, YUAN Dong, ZHANG Peng, WEI Shuguang. Steering Control Based on Fuzzy Feedforward and Feedback for Dual-motor Electric Drive Tracked Vehicle [J]. Acta Armamentarii, 2020, 41(8): 1688-1696.