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

Cooperative Motor Control for Dog Clutch Engagement of Electric Vehicles Based on Smith Predictor 基于Smith预测器的电动汽车狗爪离合器啮合电机协同控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338550

Yunchu Zhai, Ge Dong, Zhenyu Jiang, Qiong Liang, Xuesong Li, Fei Wang

引用次数: 0

Research on HIL Test Bench for New Energy Vehicle TCU* 新能源汽车TCU HIL试验台研究*

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338585

F. Zhao, Wensong Lang, Guoqing Liu, Pinglai Wang, Huiqiang Duan, Zhi-sheng You

{"title":"Research on HIL Test Bench for New Energy Vehicle TCU*","authors":"F. Zhao, Wensong Lang, Guoqing Liu, Pinglai Wang, Huiqiang Duan, Zhi-sheng You","doi":"10.1109/CVCI51460.2020.9338585","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338585","url":null,"abstract":"HIL(Hardware In Loop) test is a typical application of semi-physical simulation. It is an important function verification and test link in the development process of automotive electronic control systems. It has become a necessary means in the standardized development process, and it has been increasingly affected by various automotive OEMs(Original Equipment Manufacturer) and component manufacturers. Wide attention. This article focuses on the HIL test environment of the new energy vehicle TCU (Transmission Control Unit) control system, introduces the HIL test system architecture, software and hardware components, and the establishment process of the test environment. The test requirements are formulated according to the application scenarios of the TCU in the new energy vehicle, and the test requirements are based on the test requirements. Set up the HIL test environment. Considering that the motor speed regulation in practical applications is controlled by the vehicle controller, in order to more realistically simulate the vehicle use environment, this paper adopts the VCU(Vehicle Control Unit) and TCU dual-in-the-loop method for HIL testing, aiming at the functions of the electronic control unit controllers of the power system. Simulate the vehicle environment for testing and analyze the test results.","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":"116233619","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

Constrained Containment Control of Agents Network with Switching Topologies 交换拓扑agent网络的约束约束控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338545

Chao Liu, Jiahao Xu

引用次数: 1

Research on EEG-based Novice and Experienced Drivers' Identification Using BP Neural Network during Simulated Driving 基于脑电图的模拟驾驶中 BP 神经网络对新手和老手驾驶员的识别研究

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338490

Yingzhang Wu, Jie Zhang, Bangbei Tang, Gang Guo

{"title":"Research on EEG-based Novice and Experienced Drivers' Identification Using BP Neural Network during Simulated Driving","authors":"Yingzhang Wu, Jie Zhang, Bangbei Tang, Gang Guo","doi":"10.1109/CVCI51460.2020.9338490","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338490","url":null,"abstract":"Drivers play an important role in the transportation system. Novice drivers have insufficient driving risk awareness due to lack of driving experience, which has become a potential hazard in the traffic system. The automotive driving assistance system (ADAS) can more or less help the novice driver to avoid danger. In order to further improve the ADAS control strategy for drivers with different driving experience, it is necessary to identify novice drivers and experienced drivers. In this study, a twelve-kilometer two-way straight highway was designed as the driving scenario. Electroencephalogram(EEG) data generated in the frontal region was recorded as an indicator to evaluate the driver's perception of danger. We aim to identify novice drivers and experienced drivers by using beta waves extracted from collected EEG data when facing dangerous situations. The results indicate that the EEG features (PSD value of beta wave) extracted from the frontal region can effectively recognize the novice driver and the experienced driver through the BP neural network, and achieve a relatively high accuracy at nearly 88%.","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":"127150689","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

End-to-end control of autonomous vehicles based on deep learning with visual attention 基于视觉注意深度学习的自动驾驶车辆端到端控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338558

Zhenze Liu, Kuilin Wang, Jinliang Yu, Jingquan He

引用次数: 0

Analysis of Influencing Factors on Dynamic Performance of PEMFC Air Supply System PEMFC送风系统动态性能影响因素分析

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338511

Fengxiang Chen, Y. Pei, Zhicheng Lin, Jieran Jiao, Shiguang Liu

引用次数: 0

Conditional Integration Active Disturbance Rejection Controller for Path Tracking of Autonomous Driving Vehicles 自动驾驶车辆路径跟踪的条件积分自抗扰控制器

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338668

Zixuan Qian, Zhuoping Yu, L. Xiong, Zhiqiang Fu, Dequan Zeng

引用次数: 0

Intelligent Vehicle Environment Scene Parsing Method Based on Multi-tasking Convolutional Neural Network* 基于多任务卷积神经网络的智能车辆环境场景解析方法*

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338621

J. Lian, Yuhang Yin, Jiahao Pi, Yuekai Yang

{"title":"Intelligent Vehicle Environment Scene Parsing Method Based on Multi-tasking Convolutional Neural Network*","authors":"J. Lian, Yuhang Yin, Jiahao Pi, Yuekai Yang","doi":"10.1109/CVCI51460.2020.9338621","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338621","url":null,"abstract":"An encoder-decoder convolutional neural network architecture is presented integrating multi-class semantic segmentation and multi-object detection to improve the efficiency and depth of scene parsing of intelligent vehicle. The encoder of the network is designed as a multi-scale structure to improve real-time performance while ensuring the accuracy. The decoders of the network comprise the semantic segmentation and object detection subnetworks, which share encoder feature maps to improve computational efficiency. During the training process, we use FPS (Frames Per Second) and MIoU (Mean Intersection over Union) as the evaluation metrics of semantic segmentation, while the mAP (mean Average Precision) and FPS are used as the performance evaluation indexes of object detection. We conduct separate and joint training on the network to evaluate its performance. Experimental results show that the proposed network can realize multi-class semantic segmentation and multi-object detection simultaneously with better real-time performance and richer feature information, making it highly possible for implementation on real 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":"125691950","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

Nonsingular Fast Terminal Sliding Mode Control of LLC Resonant Converter for EV Charger 电动汽车充电器LLC谐振变换器的非奇异快速终端滑模控制

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338573

Qijun Su, Bin Duan, Dongjiang Yang, Hao Bai, Cheng Fu, Chenghui Zhang

引用次数: 3

Adaptive Estimator for Vehicle Roll and Road Bank Angles Using Inertial Sensors 基于惯性传感器的车辆侧倾角和道路倾斜角自适应估计

2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2020-12-18 DOI: 10.1109/CVCI51460.2020.9338602

Xiao Yang, Jianzhong Zhu, Zhengyu Pan, Boyuan Li, Rongrong Wang

引用次数: 2