Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338550
Yunchu Zhai, Ge Dong, Zhenyu Jiang, Qiong Liang, Xuesong Li, Fei Wang
Multiple speed transmissions are applied to electric vehicles gradually. A reverse gear mechanism using dog clutch is proposed for the inverse Automated Manual Transmission (I-AMT), and the coordination controller of the driving motor and the dog clutch is designed. Considering the characteristic of time delay in the motor control system, a control strategy based on Smith predictor is derived to increase the tracking ability and further improve the dynamic performance of the closed-loop control system. The experiment shows that compared with PID control strategy, those with Smith predictor is better in shifting comfort and reducing the machine wearing of dog clutch.
{"title":"Cooperative Motor Control for Dog Clutch Engagement of Electric Vehicles Based on Smith Predictor","authors":"Yunchu Zhai, Ge Dong, Zhenyu Jiang, Qiong Liang, Xuesong Li, Fei Wang","doi":"10.1109/CVCI51460.2020.9338550","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338550","url":null,"abstract":"Multiple speed transmissions are applied to electric vehicles gradually. A reverse gear mechanism using dog clutch is proposed for the inverse Automated Manual Transmission (I-AMT), and the coordination controller of the driving motor and the dog clutch is designed. Considering the characteristic of time delay in the motor control system, a control strategy based on Smith predictor is derived to increase the tracking ability and further improve the dynamic performance of the closed-loop control system. The experiment shows that compared with PID control strategy, those with Smith predictor is better in shifting comfort and reducing the machine wearing of dog clutch.","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":"115493731","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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338585
F. Zhao, Wensong Lang, Guoqing Liu, Pinglai Wang, Huiqiang Duan, Zhi-sheng You
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.
硬件在环测试是半物理仿真的典型应用。它是汽车电子控制系统开发过程中重要的功能验证和测试环节。它已成为标准化发展过程中的必要手段，并日益受到各汽车oem(原始设备制造商)和零部件制造商的影响。广泛关注。本文以新能源汽车变速器控制单元(Transmission Control Unit, TCU)控制系统的HIL测试环境为重点，介绍了HIL测试系统的体系结构、软硬件组成，以及测试环境的建立过程。根据TCU在新能源汽车中的应用场景制定测试要求，以测试需求为基础制定测试要求。设置HIL测试环境。考虑到实际应用中的电机调速是由车载控制器控制的，为了更逼真地模拟车辆使用环境，本文针对电力系统电子控制单元控制器的功能，采用VCU(vehicle Control Unit)和TCU双在环的方法进行HIL测试。模拟车辆环境进行测试，并分析测试结果。
{"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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338545
Chao Liu, Jiahao Xu
In this brief, the containment problem of double-integrate discrete-time agents network is investigated with control input and velocity constrains. A nonlinear projection algorithm is used to converge all follower agents into a convex hull formed by static leaders, where a scaling factor is proposed to solve the nonlinear constrains such as saturations and nonconvex constrains. Based on model transformation and Lyapunov function, the range from follower agents to the convex hull is proved to be nonincreasing under suitable assumption. Finally, after convex analysis, the containment problem is solved by the proposed algorithm with bounded time delays on condition that the union of the topology graphs contains spanning trees.
{"title":"Constrained Containment Control of Agents Network with Switching Topologies","authors":"Chao Liu, Jiahao Xu","doi":"10.1109/CVCI51460.2020.9338545","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338545","url":null,"abstract":"In this brief, the containment problem of double-integrate discrete-time agents network is investigated with control input and velocity constrains. A nonlinear projection algorithm is used to converge all follower agents into a convex hull formed by static leaders, where a scaling factor is proposed to solve the nonlinear constrains such as saturations and nonconvex constrains. Based on model transformation and Lyapunov function, the range from follower agents to the convex hull is proved to be nonincreasing under suitable assumption. Finally, after convex analysis, the containment problem is solved by the proposed algorithm with bounded time delays on condition that the union of the topology graphs contains spanning trees.","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":"114822766","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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338490
Yingzhang Wu, Jie Zhang, Bangbei Tang, Gang Guo
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%.
{"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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338558
Zhenze Liu, Kuilin Wang, Jinliang Yu, Jingquan He
In this paper, we propose an end-to-end controller for self-driving vehicles based on visual attention. Attention strategy is used to weight the high-dimensional feature information extracted by convolutional neural networks (CNNs), and then the vehicle's velocity and steering wheel angle are predicted by different recurrent neural networks (RNNs). The end-to-end controller is trained on Comma.ai dataset and can effectively reduce the mean absolute error (MAE). The result shows that compared with other models, the end-to-end control model based on visual attention can achieve better control effects of vehicle's speed and steering wheel angle.
{"title":"End-to-end control of autonomous vehicles based on deep learning with visual attention","authors":"Zhenze Liu, Kuilin Wang, Jinliang Yu, Jingquan He","doi":"10.1109/CVCI51460.2020.9338558","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338558","url":null,"abstract":"In this paper, we propose an end-to-end controller for self-driving vehicles based on visual attention. Attention strategy is used to weight the high-dimensional feature information extracted by convolutional neural networks (CNNs), and then the vehicle's velocity and steering wheel angle are predicted by different recurrent neural networks (RNNs). The end-to-end controller is trained on Comma.ai dataset and can effectively reduce the mean absolute error (MAE). The result shows that compared with other models, the end-to-end control model based on visual attention can achieve better control effects of vehicle's speed and steering wheel angle.","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":"127448784","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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338511
Fengxiang Chen, Y. Pei, Zhicheng Lin, Jieran Jiao, Shiguang Liu
Air supply system is one of the most important auxiliary subsystems of proton exchange membrane fuel cell (PEMFC). The response speed of voltage and current in fuel cell system greatly depends on the dynamic performance of air supply system. In this paper, based on AMESim software®, a fuel cell air supply system model of 72kW stack is built. The influence of air compressor response speed, buffer tank and flow resistance on the dynamic response characteristics of air supply system is analyzed, and the influence mechanism is briefly analyzed according to the simulation results.
{"title":"Analysis of Influencing Factors on Dynamic Performance of PEMFC Air Supply System","authors":"Fengxiang Chen, Y. Pei, Zhicheng Lin, Jieran Jiao, Shiguang Liu","doi":"10.1109/CVCI51460.2020.9338511","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338511","url":null,"abstract":"Air supply system is one of the most important auxiliary subsystems of proton exchange membrane fuel cell (PEMFC). The response speed of voltage and current in fuel cell system greatly depends on the dynamic performance of air supply system. In this paper, based on AMESim software®, a fuel cell air supply system model of 72kW stack is built. The influence of air compressor response speed, buffer tank and flow resistance on the dynamic response characteristics of air supply system is analyzed, and the influence mechanism is briefly analyzed according to the simulation 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":"125354775","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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338668
Zixuan Qian, Zhuoping Yu, L. Xiong, Zhiqiang Fu, Dequan Zeng
Aim at rejecting uncertainty disturbance and actuator saturation, a path tracking method is proposed for autonomous driving vehicles, which is implement by active disturbance rejection controller (ADRC) with conditional integration. Firstly, a kinematic-dynamic vehicle model is deduced for describing path tracking process. Secondly, a nonlinear extended state observer is designed to observe the uncertainty disturbance, such as external disturbance and parameter uncertainties. Finally, in order to eliminate error and reject disturbance while resisting actuator saturation, a conditional integration is developed as feedback control low. The test results of lane changing scenarios show that the proposed algorithm can track the desired path quickly and accurately compared with PID and ADRC.
{"title":"Conditional Integration Active Disturbance Rejection Controller for Path Tracking of Autonomous Driving Vehicles","authors":"Zixuan Qian, Zhuoping Yu, L. Xiong, Zhiqiang Fu, Dequan Zeng","doi":"10.1109/CVCI51460.2020.9338668","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338668","url":null,"abstract":"Aim at rejecting uncertainty disturbance and actuator saturation, a path tracking method is proposed for autonomous driving vehicles, which is implement by active disturbance rejection controller (ADRC) with conditional integration. Firstly, a kinematic-dynamic vehicle model is deduced for describing path tracking process. Secondly, a nonlinear extended state observer is designed to observe the uncertainty disturbance, such as external disturbance and parameter uncertainties. Finally, in order to eliminate error and reject disturbance while resisting actuator saturation, a conditional integration is developed as feedback control low. The test results of lane changing scenarios show that the proposed algorithm can track the desired path quickly and accurately compared with PID and ADRC.","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":"127310598","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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338621
J. Lian, Yuhang Yin, Jiahao Pi, Yuekai Yang
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.
为了提高智能汽车场景分析的效率和深度，提出了一种集多类语义分割和多目标检测于一体的编码器-解码器卷积神经网络架构。网络的编码器采用多尺度结构设计，在保证精度的同时提高了实时性。该网络的解码器包括语义分割和目标检测子网，它们共享编码器特征映射以提高计算效率。在训练过程中，我们使用FPS (Frames Per Second)和MIoU (Mean Intersection over Union)作为语义分割的评价指标，mAP (Mean Average Precision)和FPS作为目标检测的性能评价指标。我们对网络进行单独和联合训练，以评估其性能。实验结果表明，该网络能够同时实现多类语义分割和多目标检测，具有更好的实时性和更丰富的特征信息，为在真实车辆上实现提供了很大的可能性。
{"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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338573
Qijun Su, Bin Duan, Dongjiang Yang, Hao Bai, Cheng Fu, Chenghui Zhang
LLC resonant converter is widely used in electric vehicle (EV) charger for the advantages of low switching loss and high power density. However, its dynamic performance and robustness are easily influenced by multiple disturbance factors. This paper proposes a nonsingular fast terminal sliding mode (NFTSM) control strategy for the LLC resonant converter to improve the dynamic performance and robustness. First, the second-order small-signal model is obtained by the linearized and simplified large-signal mathematical model which is established based on the extended description function method. Then, the NFTSM controller is designed based on the small-signal model. And the system stability is proved by Lyapunov's stability theorem. Finally, Simulation results verify the feasibility and effectiveness of the proposed control scheme.
{"title":"Nonsingular Fast Terminal Sliding Mode Control of LLC Resonant Converter for EV Charger","authors":"Qijun Su, Bin Duan, Dongjiang Yang, Hao Bai, Cheng Fu, Chenghui Zhang","doi":"10.1109/CVCI51460.2020.9338573","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338573","url":null,"abstract":"LLC resonant converter is widely used in electric vehicle (EV) charger for the advantages of low switching loss and high power density. However, its dynamic performance and robustness are easily influenced by multiple disturbance factors. This paper proposes a nonsingular fast terminal sliding mode (NFTSM) control strategy for the LLC resonant converter to improve the dynamic performance and robustness. First, the second-order small-signal model is obtained by the linearized and simplified large-signal mathematical model which is established based on the extended description function method. Then, the NFTSM controller is designed based on the small-signal model. And the system stability is proved by Lyapunov's stability theorem. Finally, Simulation results verify the feasibility and effectiveness of the proposed control scheme.","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":"131813989","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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338602
Xiao Yang, Jianzhong Zhu, Zhengyu Pan, Boyuan Li, Rongrong Wang
This paper presents an adaptive approach to simultaneously estimate the angles of vehicle roll and road bank with off-the-shelf vehicle inertial sensors. Measured signals are firstly processed through a kinematic model based adaptive complementary filter, and then fused in a dynamic model based Kalman filter. Adaptive law is designed to suppress the undesired effect caused by transient motion and integral drift. Suspension displacement sensors were installed to accurately measure the reference value of vehicle-body roll angle, and on-vehicle experiments on uneven ground were conducted to evaluate the performance of the proposed method. The effectiveness of the estimator was approved by comparing the estimating results and the reference.
{"title":"Adaptive Estimator for Vehicle Roll and Road Bank Angles Using Inertial Sensors","authors":"Xiao Yang, Jianzhong Zhu, Zhengyu Pan, Boyuan Li, Rongrong Wang","doi":"10.1109/CVCI51460.2020.9338602","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338602","url":null,"abstract":"This paper presents an adaptive approach to simultaneously estimate the angles of vehicle roll and road bank with off-the-shelf vehicle inertial sensors. Measured signals are firstly processed through a kinematic model based adaptive complementary filter, and then fused in a dynamic model based Kalman filter. Adaptive law is designed to suppress the undesired effect caused by transient motion and integral drift. Suspension displacement sensors were installed to accurately measure the reference value of vehicle-body roll angle, and on-vehicle experiments on uneven ground were conducted to evaluate the performance of the proposed method. The effectiveness of the estimator was approved by comparing the estimating results and the reference.","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":"131890722","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：