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2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)最新文献

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Estimation of Vehicle State Based on Limited Memory Random Weighted Unscented Kalman Filter 基于有限记忆随机加权无气味卡尔曼滤波的车辆状态估计
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661257
Jingyu Hu, Yan Wang, Yongjun Yan, Yanjun Ren, Jingxiang Wang, Guo-dong Yin
{"title":"Estimation of Vehicle State Based on Limited Memory Random Weighted Unscented Kalman Filter","authors":"Jingyu Hu, Yan Wang, Yongjun Yan, Yanjun Ren, Jingxiang Wang, Guo-dong Yin","doi":"10.1109/CVCI54083.2021.9661257","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661257","url":null,"abstract":"Critical state information of the vehicle is a prerequisite for effective implementation of active safety systems. Combining on-board sensor information and advanced filters to estimate vehicle states is much more economical than direct measurement methods. However, the influence of historical measurement data on the estimation accuracy is not fully considered in the existing estimation methods. Therefore, a novel limited memory random weighted unscented Kalman filter (LMRWUKF) is proposed to address this problem. First, a nonlinear three-degree-of-freedom vehicle dynamics model is established. Then, an iterative method to update the measurement noise covariance matrix using finite measurement data is embedded into the unscented Kalman filter to form the LMRWUKF. Finally, virtual tests for two different operating conditions show that the performance of LMRWUKF is better than the unscented Kalman.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117175733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Adaptive Sliding Mode Active Disturbance Rejection Control of Quadrotor UAV for Hydropower Station Geomorphology 水电站地貌四旋翼无人机自适应滑模自抗扰控制
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661151
Mengdong Lu, Ruiyu Zhuang, Huiliang Shen, Song Xiao, Hua Chen
{"title":"Adaptive Sliding Mode Active Disturbance Rejection Control of Quadrotor UAV for Hydropower Station Geomorphology","authors":"Mengdong Lu, Ruiyu Zhuang, Huiliang Shen, Song Xiao, Hua Chen","doi":"10.1109/CVCI54083.2021.9661151","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661151","url":null,"abstract":"Aiming at the problem that the attitude control of UAV is easy to be affected by disturbance, this paper designs an adaptive fast terminal sliding mode active disturbance rejection attitude controller for quadrotor UAV combined with fast terminal sliding mode control technology and linear active disturbance rejection control (LARDC) technology. The proposed controller improves the convergence speed when the initial position is far from the equilibrium point, and realizes global fast convergence. Simulation results show that the controller has high robustness, and effectively suppresses the sliding mode chattering caused by uncertain disturbance.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115074026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intelligent Vehicle Path Planning Considering Side Slip of Surrounding Vehicles in Icy and Snowy Environment 冰雪环境下考虑周边车辆侧滑的智能车辆路径规划
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661245
Hongyan Guo, Wei Zhao, Jun Liu, Xiaoming Zhao
{"title":"Intelligent Vehicle Path Planning Considering Side Slip of Surrounding Vehicles in Icy and Snowy Environment","authors":"Hongyan Guo, Wei Zhao, Jun Liu, Xiaoming Zhao","doi":"10.1109/CVCI54083.2021.9661245","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661245","url":null,"abstract":"Under the icy and snowy environment, the road conditions are complex and changeable, especially the reduction of road adhesion coefficient, which brings great challenges to the driving safety of intelligent vehicles. Aiming at the problem that the peripheral vehicle may be unstable at any time in the ice and snow environment, a path planning method considering the side slip of the peripheral vehicle in the icy and snowy environment is proposed in this paper. Firstly, reinforcement learning and artificial potential field are combined to transform the force received by the vehicle in the artificial potential field into the objective function in reinforcement learning for reinforcement learning training. Then, because this paper studies the path planning in the icy and snowy environment, considering that the sideslip of the obstacle vehicle will bring a safety threat to the vehicle, the dynamic rectangular virtual repulsion field of the obstacle vehicle is adjusted accordingly during the sideslip. So that the vehicle will be more repulsed by the obstacle vehicle at this time, and guide the vehicle to avoid the dangerous vehicle in time. Finally, the path planning results are simulated and verified in Matlab / CarSim software. The simulation results show that the path planning method in this paper can effectively make the vehicle avoid obstacles safely and stably, and the planned path meets the dynamic requirements of the vehicle and driving stability.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115579151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Motion Planning Framework for Autonomous Vehicle with LSTM-based Predictive Model 基于lstm预测模型的自动驾驶汽车运动规划框架
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661146
Xiaolin Tang, Guichuan Zhong, Kai Yang, Jiahang Wu, Zichun Wei
{"title":"Motion Planning Framework for Autonomous Vehicle with LSTM-based Predictive Model","authors":"Xiaolin Tang, Guichuan Zhong, Kai Yang, Jiahang Wu, Zichun Wei","doi":"10.1109/CVCI54083.2021.9661146","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661146","url":null,"abstract":"Accurate motion prediction of surrounding vehicles is of great importance to enable lower risk motion planning for autonomous vehicles. In this paper, a long short-term memory-based motion prediction approach is first adopted to predict the trajectories of surrounding vehicles, and the designed Long-Short Term network is trained and tested based on the Next Generation Simulation (NGSIM) datasets. More importantly, a motion planning technique is developed based the trajectory prediction model. Specifically, to avoid collisions with surrounding vehicles on highway, an artificial potential field-based risk assessment method is presented, and the road boundaries are also considered. Subsequently, the model predictive control algorithm is utilized to consider the risk of collision, road boundaries as well as the vehicle dynamics. Finally, the simulation results show the effectiveness of the proposed method.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122618685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Secure Interactive Architecture of Cloud Driving System Based on 5G and Quantum Encryption 基于5G和量子加密的云驾驶系统安全交互架构
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661202
Cheng Teng, Wang Xiuwen, Shi Qin, Chen Jiong, Sun Lei, Hou Dengchao, Huang He, Jiang Junzhao, Wu Bofu
{"title":"Secure Interactive Architecture of Cloud Driving System Based on 5G and Quantum Encryption","authors":"Cheng Teng, Wang Xiuwen, Shi Qin, Chen Jiong, Sun Lei, Hou Dengchao, Huang He, Jiang Junzhao, Wu Bofu","doi":"10.1109/CVCI54083.2021.9661202","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661202","url":null,"abstract":"With the development of the automobile industry, the Internet and computer technology, intelligent connected vehicle have become the main direction of future automobile development. As an important part of the security of intelligent connected vehicle, the cloud driving system puts forward higher requirements for its real-time and safety. In the background of vehicle-road collaboration, this paper proposes a security interaction architecture of cloud driving system based on quantum encryption and 5G network communication technology. After testing and verification, the results show that the cloud driving system architecture has good real-time performance and reliability.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122834971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Path Planning and Control of Driverless Logistics Train 无人驾驶物流列车路径规划与控制研究
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661132
Jinxiang Feng, Bo Yang, Xiaofei Pei, Pengwei Zhou
{"title":"Research on Path Planning and Control of Driverless Logistics Train","authors":"Jinxiang Feng, Bo Yang, Xiaofei Pei, Pengwei Zhou","doi":"10.1109/CVCI54083.2021.9661132","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661132","url":null,"abstract":"Driverless logistics train is an important application of driverless vehicle in the field of cargo transportation. In this paper, firstly, the kinematics modeling of the driverless logistics train platform is carried out, and its kinematics characteristics are analyzed. Then, based on the driverless logistics train platform, a path planning algorithm based on quintic polynomial and a path tracking control strategy based on feedforward and feedback are proposed. The control method of linear quadratic regulator (LQR) is adopted in the feedback control, and its goal is to reduce the lateral error and direction deviation between the vehicle and the target path. Finally, vehicle experiments are carried out based on the driverless logistics train, and the results verify the effectiveness and accuracy of the proposed method. In addition, in the experiment on the campus road, considering the inaccurate positioning and drift of GPS in the shade of trees, the positioning method of Simultaneous Localization and Mapping (SLAM) is used, which can solve the above problems effectively.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130147703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Optimal Preview Distance Control Using Model Prediction for Autonomous Vehicle 基于模型预测的自动驾驶汽车最优预览距离控制
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661160
Cheng Li, Wei Chen, X. Luo, Fangfang Wang, Jingming Zhang, Yanlong Yang, Hongbin Sun
{"title":"Optimal Preview Distance Control Using Model Prediction for Autonomous Vehicle","authors":"Cheng Li, Wei Chen, X. Luo, Fangfang Wang, Jingming Zhang, Yanlong Yang, Hongbin Sun","doi":"10.1109/CVCI54083.2021.9661160","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661160","url":null,"abstract":"The performance of the autonomous driving control system is the key to the stability and safety of autonomous vehicles. As part of the control system, path tracking is responsible for tracking the desired trajectory. This article proposes a multi-point preview method based on the model prediction, which uses the preview distance obtained by the Stanley path tracking method to interpolate back and forth to obtain candidate preview points, which are converted into steering angles through geometric relations. Finally, selecting the optimal steering angle is selected through the designed optimal function. Different from the traditional kinematics model, this paper also predicts the steering angle at the next moment to achieve more accurate vehicle state prediction. In this way, the proposed method is well compared and analysed with the traditional Stanley controller scheme. Comparing with the proposed method, the traditional Stanley controller is not capable of keeping the steering stable as it faces the road with changeable curvature. The results show that the implementation of model predictive multi-point preview control strategy for Stanley tracking effectively improves the stability and accuracy of tracking.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122984954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Adaptive Optimization Control Strategy For Advanced Engine Thermal Management Systems 先进发动机热管理系统的自适应优化控制策略
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661207
Kai Li, Yu Zhang, Xin Liu, Jinwu Gao, B. Gao
{"title":"An Adaptive Optimization Control Strategy For Advanced Engine Thermal Management Systems","authors":"Kai Li, Yu Zhang, Xin Liu, Jinwu Gao, B. Gao","doi":"10.1109/CVCI54083.2021.9661207","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661207","url":null,"abstract":"An engine thermal management system is required to remove the waste heat from the engine’s combustion process to ensure high efficiency operation within a safe temperature range. In advanced engine thermal management systems (AETMS), electric actuators replace the traditional thermostat, mechanical coolant pump, and radiator fans to improve the temperature control accuracy and system efficiency. In the presence of unknown disturbances in the system, it is a challenge to coordinately control multiple actuators to ensure accurate temperature control and low system parasitic energy consumption. This paper develops an adaptive optimization control strategy composed of a parameter estimator, a tracking controller and a balance point optimization block. In addition, the stability of the proposed strategy is proven by Lyapunov’s stability theory. Finally, the co-simulation results between Matlab and AEMsim are presented to show the effectiveness of the proposed approach.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124145359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Improvement of Low-Cost Inertial Sensors Based on Online Estimation of Temperature Drift Error 基于温度漂移误差在线估计的低成本惯性传感器改进
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661156
Zhuoping Yu, Zhilong Xie, L. Xiong, Yishi Lu, Mengyuan Chen, Dequan Zeng
{"title":"Improvement of Low-Cost Inertial Sensors Based on Online Estimation of Temperature Drift Error","authors":"Zhuoping Yu, Zhilong Xie, L. Xiong, Yishi Lu, Mengyuan Chen, Dequan Zeng","doi":"10.1109/CVCI54083.2021.9661156","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661156","url":null,"abstract":"Improving the performance of low-cost inertial sensors is meaningful for popularization of automated driving technologies. Researchers usually utilize thermal chamber to calibrate the temperature drift errors (TDEs) of expensive inertial sensors and compensate them before using them. However, it is unacceptable for low-cost inertial sensors considering the massive calibration efforts. This paper proposes a novel method to model TDE as a state so that it can be estimated online with other states together, which can adaptively compensate different TDEs without preparation. A multi-sensor fusion system for estimating yaw angle with this idea is studied. The observability of this system is analyzed and the result shows that TDE is independent from others states when there is change of temperature. Experiments are carried out and the results reveal that the performance of inertial sensor assisted with the proposed method is better than that in normal system.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Learning End-to-End Inertial-Wheel Odometry for Vehicle Ego-Motion Estimation 学习端到端惯性车轮里程计的车辆自我运动估计
2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661121
Zhihuang Zhang, Jintao Zhao, Changyao Huang, Liang Li
{"title":"Learning End-to-End Inertial-Wheel Odometry for Vehicle Ego-Motion Estimation","authors":"Zhihuang Zhang, Jintao Zhao, Changyao Huang, Liang Li","doi":"10.1109/CVCI54083.2021.9661121","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661121","url":null,"abstract":"We present an end-to-end learning architecture for wheel speeds and inertial fusion for real-time ego-motion estimation. In contrast to the previous work, our method utilizes the neural network to learn the nonlinear vehicle model and reduce the modeling error in motion propagation. The network inputs are the wheel speeds and inertial measurements while the outputs are the 2D pose increments in vehicle frames. To enhance the learning efficiency of the network, we propose a data enhancement strategy and employ a large amount of data for training. A diversity of practical scenario tests have shown that our proposed method outperforms traditional methods in terms of accuracy and robustness, achieving an average translation error of less than 0.4m per 100m, which meets the practical application requirements and the trained model has been deployed as a front-end module for other systems on an autonomous vehicle.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125441009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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