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Cascade Optimization Control of Unmanned vehicle Path Tracking Under Harsh Driving Conditions

Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 114-125页

作者：黄迎港罗文广黄丹蓝红莉 School of Electrical and Information Engineering、guangxi key laboratory of auto parts and vehicle technology Guangxi University of Science and TechnologyLiuzhou 545006GuangxiChina School of Mechanical and automotive Engineering South China University of TechnologyGuangzhou 510641China

Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning ***,this study proposes a cascade control to solve this *** on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned *** improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving *** speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed ***,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned *** strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.

关键词： unmanned vehicles path tracking harsh driving conditions cascade control improved gray wolf optimization algorithm backpropagation neural network

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Safety Evaluation of Commercial vehicle Driving Behavior Using the AHP–CRITIC Algorithm

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 126-135页

作者：庞娜罗文广吴若园蓝红莉覃永新苏琦 School of Electrical and Information Engineering Guangxi Key Laboratory of Auto Parts and Vehicle TechnologyGuangxi University of Science and TechnologyLiuzhou 545006GuangxiChina Liuzhou OVM Machinery Co. Ltd.Liuzhou 545005GuangxiChina

To prevent and reduce road traffic accidents and improve driver safety awareness and bad driving be-haviors,we propose a safety evaluation method for commercial vehicle driving *** driving style clas-sification indexes were extracted using driving data from commercial vehicles and four primary and ten secondary safety evaluation *** on the stability of commercial vehicles transporting goods,the acceleration index is divided into three levels according to the statistical third quartile,and the evaluation expression of the safety index evaluation is *** were divided into conservative,moderate,and radical using K-means++.The weights corresponding to each index were calculated using a combination of the analytic hierarchy process(AHP)and criteria importance through intercriteria correlation(CRITIC),and the driving behavior scores of various drivers were calculated according to the safety index score *** established AHP-CRITIC safety evaluation model was verified using the actual driving behavior data of commercial vehicle *** calculation results show that the proposed evaluation model can clearly distinguish between the types of drivers with different driving styles,verifying its rationality and *** evaluation results can provide a reference for transportation management departments and enterprises.

关键词： commercial vehicle driving behavior analytic hierarchy process(AHP) criteria importance through intercriteria correlation(CRITIC) safety evaluation driving style

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