Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338595
Chongsong Hu, K. Song, H. Xie
The roller is a typical articulated multi-body vehicle with multi-degree of freedom in motion. Accurate and reliable position and heading angle measurements are important foundations for the accurate path-following of unmanned rollers. Due to the poor operation environment of the roller, the positioning signal often drifts or jumps, which affects the reliable operation of the system. To achieve reliable fault diagnostic in the positioning system, in this paper, a novel solution that combines total disturbance observation and support vector machine (SVM) classification, is proposed. A multi-body kinematic model is established with steering wheel angle and vehicle speed as inputs, and with the longitude, latitude and heading angle as outputs. The discrepancy of model estimates from the measured value is treated as total disturbance, to be estimated by the extended state observer. Then the estimated total disturbance, together with the measured position and heading angle are input into the support vector machine for faults classification. Experimental results show that the fault diagnosis accuracy is 95%, the improvement in accuracy and computational time is 9% and 12% respectively, compared with the conventional solution that only based on SVM.
{"title":"GPS Signal Fault Diagnosis for Unmanned Rollers Based on Total Disturbance Observation and Support Vector Machine","authors":"Chongsong Hu, K. Song, H. Xie","doi":"10.1109/CVCI51460.2020.9338595","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338595","url":null,"abstract":"The roller is a typical articulated multi-body vehicle with multi-degree of freedom in motion. Accurate and reliable position and heading angle measurements are important foundations for the accurate path-following of unmanned rollers. Due to the poor operation environment of the roller, the positioning signal often drifts or jumps, which affects the reliable operation of the system. To achieve reliable fault diagnostic in the positioning system, in this paper, a novel solution that combines total disturbance observation and support vector machine (SVM) classification, is proposed. A multi-body kinematic model is established with steering wheel angle and vehicle speed as inputs, and with the longitude, latitude and heading angle as outputs. The discrepancy of model estimates from the measured value is treated as total disturbance, to be estimated by the extended state observer. Then the estimated total disturbance, together with the measured position and heading angle are input into the support vector machine for faults classification. Experimental results show that the fault diagnosis accuracy is 95%, the improvement in accuracy and computational time is 9% and 12% respectively, compared with the conventional solution that only based on SVM.","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":"123167781","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/CVCI51460.2020.9338489
Jialun Cui, Zheng Chen, Jiangwei Shen, Shiquan Shen, Yonggang Liu
In this study, a nonlinear generalized predictive controller (NGPC) in a cascaded structure, combining with sliding mode disturbance observer (SDMO), is proposed to control the permanent magnet synchronous hub motor (PMSHM) with uncertainties and disturbances. The NGPC is designed on the basis of the Taylor series expansion to approximate the predictive response in finite time domain. Since NGPC cannot thoroughly remove the deviation in the load torque variation and parametric uncertainties, an improved SMDO is exploited to estimate and compensate the deviation of controller. The developed controller can fulfill the performance of strong robustness and fast dynamic response with easy regulation characteristics. The simulation results manifest the effectiveness of the designed control strategy applied to the PMSHM drive.
{"title":"Robust Cascaded Nonlinear Generalized Predictive Control with Sliding Mode Disturbance Observer for Permanent Magnet Synchronous Hub Motor","authors":"Jialun Cui, Zheng Chen, Jiangwei Shen, Shiquan Shen, Yonggang Liu","doi":"10.1109/CVCI51460.2020.9338489","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338489","url":null,"abstract":"In this study, a nonlinear generalized predictive controller (NGPC) in a cascaded structure, combining with sliding mode disturbance observer (SDMO), is proposed to control the permanent magnet synchronous hub motor (PMSHM) with uncertainties and disturbances. The NGPC is designed on the basis of the Taylor series expansion to approximate the predictive response in finite time domain. Since NGPC cannot thoroughly remove the deviation in the load torque variation and parametric uncertainties, an improved SMDO is exploited to estimate and compensate the deviation of controller. The developed controller can fulfill the performance of strong robustness and fast dynamic response with easy regulation characteristics. The simulation results manifest the effectiveness of the designed control strategy applied to the PMSHM drive.","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":"115746392","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/CVCI51460.2020.9338651
Xu Tao, Fan Jingjing, Guan Shuai, Liu Zhipeng
Unmanned vehicle can be used as a transport tool for teams and groups to accompany and follow soldiers, reduce the load of team members and identify team members accurately in real time. It is a prerequisite for the realization of control algorithm and one of the core technologies for automatic control of military vehicles. Aiming at the problem of personnel identification under the fusion perception of lidar and camera, especially the problem of multi-sensor space and time synchronization, this paper proposes a solution based on multi-sensor fusion, and designs fusion criteria of space scale, time scale and personnel identification. The experimental results show that a designed personnel identification algorithm based on multi-sensor space and time fusion can accurately identify personnel targets in complex environment, and the intersection ratio of lidar and camera fusion algorithm exceeds 95%.
{"title":"Multi-sensor Spatial and Time Scale Fusion Method for Off-road Environment Personnel Identification","authors":"Xu Tao, Fan Jingjing, Guan Shuai, Liu Zhipeng","doi":"10.1109/CVCI51460.2020.9338651","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338651","url":null,"abstract":"Unmanned vehicle can be used as a transport tool for teams and groups to accompany and follow soldiers, reduce the load of team members and identify team members accurately in real time. It is a prerequisite for the realization of control algorithm and one of the core technologies for automatic control of military vehicles. Aiming at the problem of personnel identification under the fusion perception of lidar and camera, especially the problem of multi-sensor space and time synchronization, this paper proposes a solution based on multi-sensor fusion, and designs fusion criteria of space scale, time scale and personnel identification. The experimental results show that a designed personnel identification algorithm based on multi-sensor space and time fusion can accurately identify personnel targets in complex environment, and the intersection ratio of lidar and camera fusion algorithm exceeds 95%.","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":"123806721","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}
When comparing the environmental protection and economy of different cars, it is necessary for cars to run in the same driving cycle to obtain the pollutant emission and fuel consumption. However, in the actual driving process, the performance of the vehicle may be markedly different from the performance in test cycle. In order to generate the driving cycle that can represent the actual driving process, this paper adopts the driving data of an express truck with specific driving routes to construct the typical driving cycle of a city by combining Markov chain with Monte Carlo random sampling. The random response is added in the construction process, and the variation parameter is used to simulate the sudden traffic situation. CCPV and CPV parameters are set to evaluate the generated driving cycle. Through Simulink simulation, the reliability of the generated driving cycle is verified and the influence of different statistical characteristics is determined.
{"title":"A driving cycle construction methodology combining Markov chain with variation parameters and Monte Carlo","authors":"Jiaming Xing, Yuanjian Zhang, Chong Guo, Zhuoran Hou, Peng Liu, Shibo Li","doi":"10.1109/CVCI51460.2020.9338594","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338594","url":null,"abstract":"When comparing the environmental protection and economy of different cars, it is necessary for cars to run in the same driving cycle to obtain the pollutant emission and fuel consumption. However, in the actual driving process, the performance of the vehicle may be markedly different from the performance in test cycle. In order to generate the driving cycle that can represent the actual driving process, this paper adopts the driving data of an express truck with specific driving routes to construct the typical driving cycle of a city by combining Markov chain with Monte Carlo random sampling. The random response is added in the construction process, and the variation parameter is used to simulate the sudden traffic situation. CCPV and CPV parameters are set to evaluate the generated driving cycle. Through Simulink simulation, the reliability of the generated driving cycle is verified and the influence of different statistical characteristics is determined.","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":"125405775","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/CVCI51460.2020.9338622
Jing Houhua, Liu Haifeng, Guan Yihang
Brake by wire is a key technology in the motion control level of autonomous vehicles. In order to provide a flexible control interface for the decision-making level, the hardware of the brake-by-wire controller is designed, and the active hydraulic boosting law is implemented using model-based-design method. The application verification is carried out on the experimental bench. The results show it is feasible to use the model-based-design method for the brake-by-wire controller development.
{"title":"Model Based Design and Experimental Test of Brake-By-Wire Controller","authors":"Jing Houhua, Liu Haifeng, Guan Yihang","doi":"10.1109/CVCI51460.2020.9338622","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338622","url":null,"abstract":"Brake by wire is a key technology in the motion control level of autonomous vehicles. In order to provide a flexible control interface for the decision-making level, the hardware of the brake-by-wire controller is designed, and the active hydraulic boosting law is implemented using model-based-design method. The application verification is carried out on the experimental bench. The results show it is feasible to use the model-based-design method for the brake-by-wire controller development.","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":"116419234","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/CVCI51460.2020.9338507
Xiaogang Wu, Yu Chen, Xuhui Han, Jiuyu Du, Tao Wen, Yizhao Sun
Lithium-ion batteries may be slightly overcharged due to the errors in the Battery Management System (BMS) state estimation when used in the field of vehicle power batteries, which may lead to problems such as battery performance degradation and battery stability degradation. Therefore, this paper conducts an experimental study on the influence of slightly overcharging cycles on battery performance degradation, and builds a semi-empirical capacity degradation model under slightly overcharging cycles on this basis. The experimental results show that the slightly overcharging cycle causes the capacity decay of the battery to be significantly accelerated, and its capacity decay will also cause the capacity “diving” phenomenon at the end of its life under normal cycle conditions. The slightly overcharging cycle has little effect on the internal polarization resistance of the battery. But it has a greater impact on the ohmic internal resistance due to the thickening of the SEI film.
{"title":"Analysis of performance degradation of lithium iron phosphate power battery under slightly overcharging cycles","authors":"Xiaogang Wu, Yu Chen, Xuhui Han, Jiuyu Du, Tao Wen, Yizhao Sun","doi":"10.1109/CVCI51460.2020.9338507","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338507","url":null,"abstract":"Lithium-ion batteries may be slightly overcharged due to the errors in the Battery Management System (BMS) state estimation when used in the field of vehicle power batteries, which may lead to problems such as battery performance degradation and battery stability degradation. Therefore, this paper conducts an experimental study on the influence of slightly overcharging cycles on battery performance degradation, and builds a semi-empirical capacity degradation model under slightly overcharging cycles on this basis. The experimental results show that the slightly overcharging cycle causes the capacity decay of the battery to be significantly accelerated, and its capacity decay will also cause the capacity “diving” phenomenon at the end of its life under normal cycle conditions. The slightly overcharging cycle has little effect on the internal polarization resistance of the battery. But it has a greater impact on the ohmic internal resistance due to the thickening of the SEI film.","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":"122431820","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/CVCI51460.2020.9338567
Y. Zhang, Zhichao Fu, Qihong Chen, Liyan Zhang, Keliang Zhou, Zhihua Deng
Proton exchange membrane fuel cell (PEMFC) is an environmentally friendly and efficient power generation device. It offers promising advantages over conventional power sources in backup power supplies, distributed generation and vehicle power. A rapid response to the actual power required by load is of great significance to improve the economy and efficiency of the system. However, due to various uncertainties such as frequent disturbances and inaccurate model, the net power control has certain challenges. Therefore, a data-driven nonlinear subspace identification method is developed to build the model of net power. A segmented and consecutive step response of net power for PEMFC system are identified and analyzed, the models are verified by high-fidelity simulation data. Data-driven active disturbance rejection control (ADRC) algorithm is developed to control the model. Internal and external disturbances are considered as a total term, which is estimated and compensated by real-time input-output data and ADRC, respectively. Compared with the integral absolute error of the conventional proportion integral and proportion integral derivative control, the performance of ADRC is improved by about 89.81 % and 78.92%, respectively. Therefore, the proposed ADRC can improve the dynamic performance of PEMFC system in terms of set-point tracking performance, disturbance rejection performance and robustness.
{"title":"Data-driven active disturbance rejection net power control of proton exchange membrane fuel cell*","authors":"Y. Zhang, Zhichao Fu, Qihong Chen, Liyan Zhang, Keliang Zhou, Zhihua Deng","doi":"10.1109/CVCI51460.2020.9338567","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338567","url":null,"abstract":"Proton exchange membrane fuel cell (PEMFC) is an environmentally friendly and efficient power generation device. It offers promising advantages over conventional power sources in backup power supplies, distributed generation and vehicle power. A rapid response to the actual power required by load is of great significance to improve the economy and efficiency of the system. However, due to various uncertainties such as frequent disturbances and inaccurate model, the net power control has certain challenges. Therefore, a data-driven nonlinear subspace identification method is developed to build the model of net power. A segmented and consecutive step response of net power for PEMFC system are identified and analyzed, the models are verified by high-fidelity simulation data. Data-driven active disturbance rejection control (ADRC) algorithm is developed to control the model. Internal and external disturbances are considered as a total term, which is estimated and compensated by real-time input-output data and ADRC, respectively. Compared with the integral absolute error of the conventional proportion integral and proportion integral derivative control, the performance of ADRC is improved by about 89.81 % and 78.92%, respectively. Therefore, the proposed ADRC can improve the dynamic performance of PEMFC system in terms of set-point tracking performance, disturbance rejection performance and robustness.","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":"116730412","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/CVCI51460.2020.9338662
Guan Shuai, Ma Wenlun, Fan Jingjing, Liu Zhipeng
Aiming at the problems of high cost and limited installation of traditional unmanned vehicle environment perception methods, this paper proposes a method of personnel identification and distance measurement based on the fusion of YOLOv4 and binocular stereo vision. Through the annotation of the data set, the Darknet deep learning framework is used to train and recognize the personnel, and the binocular camera disparity data is used for personnel distance detection. The experimental results show that the recognition accuracy of this method is 0.941 and the distance error is less than 5%, which can meet the task requirements of unmanned vehicle and provide technical support for solving the environment perception problems of autonomous driving vehicle.
{"title":"Target Recognition and Range-measuring Method based on Binocular Stereo Vision","authors":"Guan Shuai, Ma Wenlun, Fan Jingjing, Liu Zhipeng","doi":"10.1109/CVCI51460.2020.9338662","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338662","url":null,"abstract":"Aiming at the problems of high cost and limited installation of traditional unmanned vehicle environment perception methods, this paper proposes a method of personnel identification and distance measurement based on the fusion of YOLOv4 and binocular stereo vision. Through the annotation of the data set, the Darknet deep learning framework is used to train and recognize the personnel, and the binocular camera disparity data is used for personnel distance detection. The experimental results show that the recognition accuracy of this method is 0.941 and the distance error is less than 5%, which can meet the task requirements of unmanned vehicle and provide technical support for solving the environment perception problems of autonomous driving vehicle.","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":"124607164","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}
The object detection based on deep learning is an important application in the field of vehicle environment perception, which has been a hot topic in recent years. We propose a novel improved Yolov3-tiny to implement more accurate object detection for the objects in traffic scenes. We employ K-means algorithm to cluster the common objects in traffic scenes to obtain a suitable size and numbers of anchor box. In addition, we modify modifying detection scale and the backbone network structure of Yolov3-tiny, improving the detection accuracy for small object. The stereo vision is also introduced to improve the accuracy of boundary location. Experiments results demonstrate that the improved yolo-tiny has higher accuracy than the original algorithm and it also meet the requirement of real-time performance.
{"title":"Object detection algorithm based on improved Yolov3-tiny network in traffic scenes","authors":"Zhenghao Wang, Linhui Li, Lei Li, Jiahao Pi, Shuoxian Li, Yafu Zhou","doi":"10.1109/CVCI51460.2020.9338478","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338478","url":null,"abstract":"The object detection based on deep learning is an important application in the field of vehicle environment perception, which has been a hot topic in recent years. We propose a novel improved Yolov3-tiny to implement more accurate object detection for the objects in traffic scenes. We employ K-means algorithm to cluster the common objects in traffic scenes to obtain a suitable size and numbers of anchor box. In addition, we modify modifying detection scale and the backbone network structure of Yolov3-tiny, improving the detection accuracy for small object. The stereo vision is also introduced to improve the accuracy of boundary location. Experiments results demonstrate that the improved yolo-tiny has higher accuracy than the original algorithm and it also meet the requirement of real-time performance.","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":"124172215","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/CVCI51460.2020.9338620
Yan Cui, Siqi Li, Yue Wang, Bolin Gao
With the development of Intelligent Connected Vehicles (ICVs), Cloud Control Platform is becoming an important part to compute driving strategies. However, when strategies are put to cloud, some of vehicle manufacturers' private data must be sent to the cloud, like Engine Map, which are core data for vehicle manufactures. How to protect these data has been the largest obstacle for the ICVs. Thus, this paper proposes a new framework in which Fully Homomorphic Encryption (FHE) and Blockchain technology are combined to compute encryption data on the cloud and record trails of cloud request. In this framework, private data can be protected, the scope of data usage will be limited, and at the same time, the execution of specific type of computations with encryption data on the cloud are fulfilled. In the end, with the help of Simple Encrypted Arithmetic Library (SEAL) developed by Microsoft Research, and IBM blockchain framework Hyperledger Fabric, this framework is verified to be feasible to build a trustworthy ICVs cloud computing system.
{"title":"The Data Protection of Intelligent Connected Vehicles Cloud Control Framework Using Fully Homomorphic Encryption","authors":"Yan Cui, Siqi Li, Yue Wang, Bolin Gao","doi":"10.1109/CVCI51460.2020.9338620","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338620","url":null,"abstract":"With the development of Intelligent Connected Vehicles (ICVs), Cloud Control Platform is becoming an important part to compute driving strategies. However, when strategies are put to cloud, some of vehicle manufacturers' private data must be sent to the cloud, like Engine Map, which are core data for vehicle manufactures. How to protect these data has been the largest obstacle for the ICVs. Thus, this paper proposes a new framework in which Fully Homomorphic Encryption (FHE) and Blockchain technology are combined to compute encryption data on the cloud and record trails of cloud request. In this framework, private data can be protected, the scope of data usage will be limited, and at the same time, the execution of specific type of computations with encryption data on the cloud are fulfilled. In the end, with the help of Simple Encrypted Arithmetic Library (SEAL) developed by Microsoft Research, and IBM blockchain framework Hyperledger Fabric, this framework is verified to be feasible to build a trustworthy ICVs cloud computing system.","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":"125463557","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}
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