2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)最新文献_第10页

Vehicle State Estimation Based on Adaptive State Transition Model 基于自适应状态转移模型的车辆状态估计

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338645

Feihua Huang, Yan Gao, Chunyun Fu, A. Gostar, R. Hoseinnezhad, Minghui Hu

{"title":"Vehicle State Estimation Based on Adaptive State Transition Model","authors":"Feihua Huang, Yan Gao, Chunyun Fu, A. Gostar, R. Hoseinnezhad, Minghui Hu","doi":"10.1109/CVCI51460.2020.9338645","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338645","url":null,"abstract":"The performance of vehicle chassis control systems relies on the accuracy of input information to the control systems. Some important vehicle states which are necessary for chassis control cannot be directly measured at low cost, such as the vehicle longitudinal and lateral velocities. In the existing literature, many vehicle state estimation solutions are designed based on vehicle dynamic models. These models inevitably involve the acquisition of tire forces which cannot be easily measured or estimated. In this paper, a vehicle state estimator is proposed based on a straightforward vehicle kinematic model, which does not rely on any tire force information. The complexity and computation load of the proposed state estimator is low. Besides, to ensure competitive estimation performance, the state transition model used in this estimator is designed to be adaptive to the on-board sensor measurements. In the simulation studies, the proposed estimator is able to provide accurate estimation results under different simulation conditions, which verifies the effectiveness of the proposed vehicle state estimator.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126132965","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

Optimal design for Flux-intensifying Permanent Magnet Machine Based on Neural Network and Multi-objective optimization 基于神经网络和多目标优化的增磁永磁电机优化设计

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338647

Qiang Ai, Hongqian Wei, Youtong Zhang

引用次数: 1

Objective Evaluation for the Driving Comfort of Vehicles Based on BP Neural Network 目的基于BP神经网络的汽车驾驶舒适性评价

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338661

Shuai Zhang, Guidong Yang, Yafei Wang, Qinghui Ji, Huimin Zhang

{"title":"Objective Evaluation for the Driving Comfort of Vehicles Based on BP Neural Network","authors":"Shuai Zhang, Guidong Yang, Yafei Wang, Qinghui Ji, Huimin Zhang","doi":"10.1109/CVCI51460.2020.9338661","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338661","url":null,"abstract":"Driving comfort, which is mainly influenced by vibration and shock, is an essential factor to evaluate the performance of intelligent vehicles. The evaluation methods of driving comfort mainly contain subjective and objective evaluation. Subjective evaluation is time-consuming, expensive and sensitive to personal feelings. And objective evaluation is difficult to exactly define the relationship between objective parameters and driving comfort. In order to combine the advantages of subjective and objective evaluation, a neural network that adopt objective indicators as input and subjective ratings as output was established for evaluating driving comfort. First, a road test with about 9000 km was conducted and key parameters of vehicle status were recorded, as well as subjective ratings. Secondly, 25,165 segments were extracted from the naturalistic driving data. Then, total weighted root-mean-square accelerations of all segments were computed according to ISO 2631–1997 Standard. And the result shows that the comfort levels calculated by weighted root-mean-square accelerations cannot match the subjective ratings given by professional evaluators very well. Finally, a 20-128-256-256-128-6 BP neural network was established and trained. And the accuracy of evaluation based on neural network is better than evaluation based on weighted root-mean-square value. The result reveals that it is feasible to establish a neural network model based on collected naturalistic driving data to evaluate the driving comfort of vehicles.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124087097","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

Trajectory-Following Control of Mecanum-Wheeled AGV Using Fuzzy Nonsingular Terminal Sliding Mode 基于模糊非奇异末端滑模的机动轮式AGV轨迹跟踪控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338561

Zhe Sun, Shujie Hu, Nengzhuo Li, Defeng He

引用次数: 0

Evaluating Safety of Mechanisms that Transit Control from Autonomous Systems to Human Drivers 评估从自动系统到人类驾驶员的过渡控制机制的安全性

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338629

Zhishuai Yin, Yuwei Pan

引用次数: 0

Adaptive Sensor Fusion of Camera, GNSS and IMU for Autonomous Driving Navigation 自动驾驶导航中相机、GNSS和IMU自适应传感器融合

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338655

Weining Ren, Kun Jiang, Xinxin Chen, Tuopu Wen, Diange Yang

引用次数: 6

A New Scheme for Semi-active Suspension Control based on BP Neural Network Model of Magnetorheological Damper 基于BP神经网络模型的磁流变减振器半主动悬架控制新方案

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338564

Honghui Zhang, Zhiyuan Zou, Hang Su

引用次数: 0

Spatiotemporal-rights-based coordinate control of isolated intersections under i-VICS i-VICS下基于时空权的孤立交叉口坐标控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338560

Song Yan, Yi Zhang, Jun-li Wang, X. Pei

{"title":"Spatiotemporal-rights-based coordinate control of isolated intersections under i-VICS","authors":"Song Yan, Yi Zhang, Jun-li Wang, X. Pei","doi":"10.1109/CVCI51460.2020.9338560","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338560","url":null,"abstract":"Most of the existing researches only consider vehicles and signals as control objects, and there are also problems of loss of space and time resources caused by unreasonable distribution of spatiotemporal-right. In this paper, an overall collaborative control model for intersections considering the distribution of spatiotemporal right, vehicle trajectory and signal timing was established. A solution algorithm for the assignment of spatiotemporal-rights based on decision tree C4.5 is proposed. A high-dimensional solution based on genetic algorithm and an enumerated low-dimensional solution for signal timing and vehicle trajectory optimization are proposed respectively. Finally, an overall control model including the phase and lane, signal timing and vehicle trajectory was established. The simulation program was developed with python3.7, and the effectiveness of algorithm proposed in this paper was verified by experiments. When flow intensity is 0.23, the algorithm has the best improvement effect, the high-dimensional and low-dimensional algorithms can reduce the delay by 57.6% and 44.8% respectively. It also verified that the algorithm has better adaptability to the change of traffic demand than the algorithm that only considers the vehicle trajectory or signal timing.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129266229","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 Tube-based Model Predictive Control for Vehicle Active Suspension System 基于自适应管的车辆主动悬架模型预测控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338658

Mingxin Kang, Ran Chen, Yuzhe Li

引用次数: 0

Energy-optimal Braking Velocity Planning of Connected Electric Vehicle 互联电动汽车能量最优制动速度规划

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338472

Haoxuan Dong, Weichao Zhuang, Yan Wang, Haonan Ding, Guo-dong Yin

{"title":"Energy-optimal Braking Velocity Planning of Connected Electric Vehicle","authors":"Haoxuan Dong, Weichao Zhuang, Yan Wang, Haonan Ding, Guo-dong Yin","doi":"10.1109/CVCI51460.2020.9338472","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338472","url":null,"abstract":"To improve the regeneration energy of electric vehicle, an energy-optimal braking strategy is developed. First, the vehicle braking intention is accessed by using vehicle-to-everything communication, i.e., braking distance and terminal velocity. Then, an optimal control problem with consideration of braking intention is formulated for maximizing regeneration energy. The control problem is solved by distance-based dynamic programming algorithm to plan the energy-optimal braking velocity. Finally, the effectiveness of proposed strategy is evaluated by simulation. The results show the regeneration energy efficiency of proposed strategy achieves improvement is over 10% compared with the constant speed strategy. Furtherly, the energy-optimal braking suggestions is investigated based on several traffic scenarios, i.e., a larger braking force in a high-velocity range can reduce vehicle resistance and make full use of motor generation power; the braking force was adjusted in moderated-velocity range for reducing friction braking, and a larger braking force should be used for parking quickly.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137730","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