Humanoid-driven Control Network Model for Longitudinal Autonomous Driving

doi:10.3901/JME.2023.18.251

Abstract

Abstract: Autonomous vehicles have become a current research hotspot in the automotive industry. Motion control algorithms remarkably determine the safety and passenger acceptance of autonomous vehicles. The accuracy of autonomous driving control algorithms is increasing, yet their consistency with the driver's control style is still low, which can reduce the driver's experience and acceptance. In addition, existing methods still have a large number of parameters that need to be manually calibrated, which leads to a high workload in algorithm deployment. A vehicle longitudinal drive control human-like neural network(LCN) is proposed, and a control model with humanoid mechanism is constructed based on it, which has a highly homogeneous control style and is capable of self-learning calibration of parameters. The design of the LCN is based on the analysis of human driving behavior. It combines the driver's control mechanism with a data-driven approach, and incorporates tolerance control characteristics and time-delay response characteristics in the driver's mechanism in the network architecture. The experimental results show that the control style of this model is closer to that of humans, and it is able to achieve self-estimation of vehicle dynamics and self-calibration of parameters.

Key words: autonomous vehicles, humanoid mechanism, self-learning calibration, motion control, tolerance control

CLC Number:

U46
TP399

GAO Zhenhai, YU Tong, SUN Tianjun, TANG Minghong, GAO Fei, ZHAO Rui. Humanoid-driven Control Network Model for Longitudinal Autonomous Driving[J]. Journal of Mechanical Engineering, 2023, 59(18): 251-262.

References

[1] PADEN B,CAP M,YONG S,et al. A survey of motion planning and control techniques for self-driving urban vehicles[J]. IEEE Transactions on Intelligent Vehicles,2016,1(1):33-55.
[2] CHEN Yan,WANG Junmin. Adaptive vehicle speed control with input injections for longitudinal motion independent road frictional condition estimation[J]. IEEE Transactions on Vehicular Technology,2011,60(3):839-848.
[3] SOUZA F D,STERN R. Calibrating microscopic car-following models for adaptive cruise control vehicles:Multiobjective approach[J]. Journal of Transportation Engineering Part a Systems,2021,147(1):04020150.
[4] YAO Di,ULBRICHT P,TONUTTI S,et al. A novel approach for experimental identification of vehicle dynamic parameters[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2020,234(10-11):2634-2648.
[5] DIELS C,BOS J E,HOTTELART K,et al. Motion sickness in automated vehicles:The elephant in the room[J]. Road Vehicle Automation 3,2016:121-129.
[6] HUANG Chao,LÜ Chen,HANG Peng,et al. Toward safe and personalized autonomous driving:Decision-making and motion control with DPF and CDT techniques[J]. IEEE/ASME Transactions on Mechatronics,2021,26(2):611-620.
[7] GERDES J C,HEDRICK J K. Vehicle speed and spacing control via coordinated throttle and brake actuation-sciencedirect[J]. Control Engineering Practice,1997,5(11):1607-1614.
[8] SHAKOURI P,ORDYS A,LAILA D S,et al. Adaptive cruise control system:Comparing gain-scheduling PI and LQ controllers[J]. IFAC Proceedings,2011,44(1):12964-12969.
[9] ZHU Min,CHEN Huiyan,XIONG Guangming. A model predictive speed tracking control approach for autonomous ground vehicles[J]. Mechanical Systems & Signal Processing,2017,87:138-152.
[10] ATTIA R,ORJUELA R,BASSET M. Combined longitudinal and lateral control for automated vehicle guidance[J]. Vehicle System Dynamics,2014,52(2):261-279.
[11] ZHOU Xianjie,VENHOVENS P. Calibration of vehicle dynamics and stability control model[J]. International Journal of Vehicle Systems Modelling & Testing,2016,11(4):285.
[12] HODGSON G,BEST M C. A Parameter identifying a kalman filter observer for vehicle handling dynamics[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2006,220(8):1063-1072.
[13] HONG S,LEE C,BORRELLI F,et al. A novel approach for vehicle inertial parameter identification using a dual kalman filter[J]. IEEE Transactions on Intelligent Transportation Systems,2015,16(1):151-161.
[14] LI Wenfei,LI Huiyun,XU Kun,et al. Estimation of vehicle dynamic parameters based on the two-stage estimation method[J]. Sensors (Basel,Switzerland),2021,21(11):3711.
[15] WENZEL T A,BURNHAM K J,BLUNDELL M V,et al. Dual extended Kalman filter for vehicle state and parameter estimation[J]. Vehicle System Dynamics,2006,44(2):153-171.
[16] GAO Zhenhai,YAN Wei,LI Hongjian. Design of the time-gap-dependent robust headway control algorithm for ACC vehicles[J]. International Journal of Vehicle Design,2016,70(4):325.
[17] ZHU Fan,XU Xin,MA Lin,et al. Autonomous driving vehicle control auto-calibration system:An industry-level,data-driven and learning-based vehicle longitudinal dynamic calibrating algorithm[C]//2020 IEEE Intelligent Vehicles Symposium (IV),2020:391-397.
[18] XIAO Xuesu,BISWAS J,STONE P. Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain[J]. IEEE Robotics and Automation Letters,2021,6(3):6054-6060.
[19] DUAN Benming,WANG Qingnian,ZENG Xiaohua,et al. Calibration methodology for energy management system of a plug-in hybrid electric vehicle[J]. Energy Conversion and Management,2017,136:240-248.
[20] GAO Zhenhai,SUN Tianjun,ZHOU Shuhui,et al. Vehicle automatic iterative learning control based on drivers' starting behavior[C]//2019 Chinese Automation Congress (CAC). 2019.
[21] TAHERIAN S,KUUTTI S,VISCA M,et al. Self-adaptive torque vectoring controller using reinforcement learning[C]//2021 IEEE International Intelligent Transportation Systems Conference,ITSC 2021,September 19,2021-September 22,2021,2021:172-179.
[22] CHIANG H-T L,HSU J,FISER M,et al. RL-RRT:Kinodynamic motion planning via learning reachability estimators from rl policies[J]. IEEE Robotics and Automation Letters,2019,4(4):4298-4305.
[23] ZHANG Yu,TINO P,LEONARDIS A,et al. A survey on neural network interpretability[J]. IEEE Transactions on Emerging Topics in Computational Intelligence,2021,5(5):726-742.
[24] SALVUCCI,DARIO D. Modeling driver behavior in a cognitive architecture[J]. Human Factors,2006,48(2):362-380.
[25] TSIMHONI O,LIU Yili. Modeling steering using the queueing network-model human processor (QN-MHP)[C]//Proceedings of the Human Factors and Ergonomics Society Annual Meeting,2003:1875-1879.
[26] LINDORFER M,MECKLENBRAUKER C F,OSTERMAYER G. Modeling the imperfect driver:Incorporating human factors in a microscopic traffic model[J]. IEEE Transactions on Intelligent Transportation Systems,2018,19(9):2856-2870.
[27] LIAO Yuan,LI S E,WANG Wenjun,et al. Detection of driver cognitive distraction:A comparison study of stop-controlled intersection and speed-limited highway[J]. IEEE Transactions on Intelligent Transportation Systems,2016,17(6):1628-1637.
[28] 李松,许源源. 政策议程、传播与注意力:基于心理视角的分析[J]. 湖南社会科学,2018(6):83-91. LI Song,XU Yuanyuan. Policy agenda,communication and attention:Based on psychological perspective analysis[J]. Social Sciences in Hunan,2018(6):83-91.
[29] NARANJO J E,GONZALEZ C,GARCIA R,et al. Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver[J]. IEEE Transactions on Intelligent Transportation Systems,2008,9(3):438-450.
[30] PARK J,SANDBERG I W. Universal approximation using radial-basis-function networks[J]. Neural Comput,1991,3(2):246-257.
[31] JACKSON I. Convergence properties of radial basis functions[J]. Constructive Approximation,1988,4(1):243-264.
[32] FEICHTENHOFER C,FAN H,MALIK J,et al. SlowFast networks for video recognition[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV),2019.
[33] XIE Siyue,HU Haifeng,CHEN Yizhen. Facial expression recognition with two-branch disentangled generative adversarial network[J]. IEEE Transactions on Circuits and Systems for Video Technology,2021,31(6):26.
[34] ZANGENEH E,RAHMATI M,MOHSENZADEH Y. Low resolution face recognition using a two-branch deep convolutional neural network architecture[J]. Expert Systems with Applications,2020,139:112854.
[35] XIE Ju,XU Xing,WANG Feng,et al. Modeling adaptive preview time of driver model for intelligent vehicles based on deep learning[J]. Proceedings of the Institution of Mechanical Engineers,Part I:Journal of Systems and Control Engineering,2021.
[36] RAMANISHKA V,CHEN Y T,MISU T,et al. Toward driving scene understanding:A dataset for learning driver behavior and causal reasoning[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),2018.
[37] ALT H,GODAU M. Computing the Fréchet distance between two polygonal curves[J]. International Journal of Computational Geometry & Applications,1995,5(1):75-91.