Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338604
Jinbo Wang, Yanfei Gao, Gaowei Xu, Fengyan Yi
In order to obtain the accuracy of vehicle parameters and occupant injury information after collision, and reduce the high cost of the sample vehicle crash test and the uncertain factors in the collision process, build rigid body model and multi-rigid body model by PC-Crash platform of traffic accident reconstruction software. Set the parameters and initial motion state of the model, and carry out the simulation and reconstruction of the traffic accident collision, which can obtain instantaneous motion state parameters of the collision vehicle and occupant. To simulate and demonstrate the process of the traffic accident, comparing the demonstration results with the actual traffic accidents, and the reduction rate of the real motion pattern is more than 95%,so that the effectiveness of this method is verified. It provides a reference for the better use of PC-Crash software to realize the simulation analysis of occupant's frontal injury, and provides a theoretical basis for accident treatment.
{"title":"Simulation analysis of occupant frontal collision damage*","authors":"Jinbo Wang, Yanfei Gao, Gaowei Xu, Fengyan Yi","doi":"10.1109/CVCI51460.2020.9338604","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338604","url":null,"abstract":"In order to obtain the accuracy of vehicle parameters and occupant injury information after collision, and reduce the high cost of the sample vehicle crash test and the uncertain factors in the collision process, build rigid body model and multi-rigid body model by PC-Crash platform of traffic accident reconstruction software. Set the parameters and initial motion state of the model, and carry out the simulation and reconstruction of the traffic accident collision, which can obtain instantaneous motion state parameters of the collision vehicle and occupant. To simulate and demonstrate the process of the traffic accident, comparing the demonstration results with the actual traffic accidents, and the reduction rate of the real motion pattern is more than 95%,so that the effectiveness of this method is verified. It provides a reference for the better use of PC-Crash software to realize the simulation analysis of occupant's frontal injury, and provides a theoretical basis for accident treatment.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130727563","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/CVCI51460.2020.9338626
Wenjun Liu, Guang Chen, Alois Knoll
Model uncertainties and external disturbances can inevitably affect vehicle dynamic control accuracy and even cause the vehicle system to be unstable and unsafe. Therefore, vehicle dynamic controller must be able to suppress the influence of model uncertainties and external disturbances on vehicle dynamic control performance. To this aim, we design a matrix inequalities (both bilinear matrix inequalities (BMIs) and linear matrix inequalities (LMIs) are involved) based robust model predictive controller for vehicle dynamic control. Robust positive invariant (RPI) set is used to guarantee the controller is robust and to construct the matrix inequality equations. We test the usefulness of the proposed controller via a numerical example.
{"title":"Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties and External Disturbances*","authors":"Wenjun Liu, Guang Chen, Alois Knoll","doi":"10.1109/CVCI51460.2020.9338626","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338626","url":null,"abstract":"Model uncertainties and external disturbances can inevitably affect vehicle dynamic control accuracy and even cause the vehicle system to be unstable and unsafe. Therefore, vehicle dynamic controller must be able to suppress the influence of model uncertainties and external disturbances on vehicle dynamic control performance. To this aim, we design a matrix inequalities (both bilinear matrix inequalities (BMIs) and linear matrix inequalities (LMIs) are involved) based robust model predictive controller for vehicle dynamic control. Robust positive invariant (RPI) set is used to guarantee the controller is robust and to construct the matrix inequality equations. We test the usefulness of the proposed controller via a numerical example.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133946869","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}
Highly automated vehicle has the possibility in getting stuck with edge scenarios where the automation cannot handle. Under this circumstance, sending out a takeover request and dragging the driver back into the control loop are required to avoid traffic accidents. Among various possible modalities for alerting drivers about take-over requests, vibrotactile alerts provide significant advantages. A driver-in-the-loop and hardware-in-the loop driving simulator was designed for the investigation of take-over performance. In this simulator, take-over signal was provided the vibration motors embedded in the vibrotactile seat. Moreover, body pressure mapping test illustrated that the vibration motors fixed in the vibrotactile seat would not reduce seating comfort. Twenty-four vibration patterns were generated via the vibration motors embedded in the backrest and cushion of the vibrotactile seat. Besides, Eighteen participants were recruited to take part in the experiment, which consisted of three sessions: 1) baseline (no driving task), 2) HAD (driving a highly automated vehicle and getting ready for the respond to the take-over request), 3) N-back (performing the same task with mental demanding task added in). Specifically, in baseline session, participants need the only answer regarding the type of vibration pattern. However in HAD and N-Back session, participants had to perform the maneuver (steering left/right or braking) according to the coding directional information of vibration patterns. Correct response rate and reaction time of each participant in each session were recorded and analysed. The results indicated that dynamic patterns yielded significantly higher correct response rate than static patterns. In addition, reaction times for dynamic patterns were faster than those for static patterns, but the effect was not statistically significant. Moreover, ANOVA tests illustrated that mental-demanding non-driving task had no significant effect on take-over performance.
{"title":"Vibrotactile Take-over Requests in Highly Automated Driving","authors":"Duanfeng Chu, Rukang Wang, Ying Deng, Lingping Lu, Chaozhong Wu","doi":"10.1109/CVCI51460.2020.9338667","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338667","url":null,"abstract":"Highly automated vehicle has the possibility in getting stuck with edge scenarios where the automation cannot handle. Under this circumstance, sending out a takeover request and dragging the driver back into the control loop are required to avoid traffic accidents. Among various possible modalities for alerting drivers about take-over requests, vibrotactile alerts provide significant advantages. A driver-in-the-loop and hardware-in-the loop driving simulator was designed for the investigation of take-over performance. In this simulator, take-over signal was provided the vibration motors embedded in the vibrotactile seat. Moreover, body pressure mapping test illustrated that the vibration motors fixed in the vibrotactile seat would not reduce seating comfort. Twenty-four vibration patterns were generated via the vibration motors embedded in the backrest and cushion of the vibrotactile seat. Besides, Eighteen participants were recruited to take part in the experiment, which consisted of three sessions: 1) baseline (no driving task), 2) HAD (driving a highly automated vehicle and getting ready for the respond to the take-over request), 3) N-back (performing the same task with mental demanding task added in). Specifically, in baseline session, participants need the only answer regarding the type of vibration pattern. However in HAD and N-Back session, participants had to perform the maneuver (steering left/right or braking) according to the coding directional information of vibration patterns. Correct response rate and reaction time of each participant in each session were recorded and analysed. The results indicated that dynamic patterns yielded significantly higher correct response rate than static patterns. In addition, reaction times for dynamic patterns were faster than those for static patterns, but the effect was not statistically significant. Moreover, ANOVA tests illustrated that mental-demanding non-driving task had no significant effect on take-over performance.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533734","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/CVCI51460.2020.9338596
Hang You, Jun Fu, Xi Li
Fuel cell vehicles are a trend in the development of new energy vehicles in the future. However, the degradation of PEMFC has always been one of the reasons restricting its development. This paper studies a PEMFC degradation model based on ECSA to determine the appropriate working state to reduce the rapid degradation of PEMFC. This model mainly simulates the dissolution and maturation of platinum clusters in the catalytic layer and explains the decline of ECSA. The simulation results show that the model can well correspond to the phenomenon of accelerated aging of the fuel cell due to variable load under vehicle operating conditions. It is suggested that the model facilitates the development of a hybrid energy management system for vehicles aimed at fuel cell degradation.
{"title":"Research on ECSA Degradation Model for PEM Fuel Cell under Vehicle Conditions","authors":"Hang You, Jun Fu, Xi Li","doi":"10.1109/CVCI51460.2020.9338596","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338596","url":null,"abstract":"Fuel cell vehicles are a trend in the development of new energy vehicles in the future. However, the degradation of PEMFC has always been one of the reasons restricting its development. This paper studies a PEMFC degradation model based on ECSA to determine the appropriate working state to reduce the rapid degradation of PEMFC. This model mainly simulates the dissolution and maturation of platinum clusters in the catalytic layer and explains the decline of ECSA. The simulation results show that the model can well correspond to the phenomenon of accelerated aging of the fuel cell due to variable load under vehicle operating conditions. It is suggested that the model facilitates the development of a hybrid energy management system for vehicles aimed at fuel cell degradation.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134383692","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/CVCI51460.2020.9338597
Xianjian Jin, Zeyuan Yan, Hang Yang, Qikang Wang, Guo-dong Yin
For the application of autonomous ground vehicle (AGV) operating in unstructured environment, a path planning method based on an improved goal-biased Rapidly-exploring Random Trees (bias-RRT) is proposed. The algorithm combines random sampling with numerical optimization to achieve fast convergence speed and satisfy constraints. KD-Tree and potential field of the environment are implemented to increase the sampling efficiency, and cubic B-splines are used to smooth the path for better tracking performance. The algorithm improves the efficiency of searching while guarantee safety and quality of the planned path. Simulation results verify the effectiveness of the proposed method.
{"title":"A Goal-Biased RRT Path Planning Approach for Autonomous Ground Vehicle","authors":"Xianjian Jin, Zeyuan Yan, Hang Yang, Qikang Wang, Guo-dong Yin","doi":"10.1109/CVCI51460.2020.9338597","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338597","url":null,"abstract":"For the application of autonomous ground vehicle (AGV) operating in unstructured environment, a path planning method based on an improved goal-biased Rapidly-exploring Random Trees (bias-RRT) is proposed. The algorithm combines random sampling with numerical optimization to achieve fast convergence speed and satisfy constraints. KD-Tree and potential field of the environment are implemented to increase the sampling efficiency, and cubic B-splines are used to smooth the path for better tracking performance. The algorithm improves the efficiency of searching while guarantee safety and quality of the planned path. Simulation results verify the effectiveness of the proposed method.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132550763","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/CVCI51460.2020.9338538
Zhuoyue Wu, G. Zhuo, Feng Xue
Recently, self-supervised method has become an increasingly significant branch of depth estimation task, especially in the realm of autonomous driving applications. However, current per-pixel depth maps yielded from RGB images still suffer from uncertain scale factor generated by the nature of monocular image sequences, which further leads to the insufficiency in practical use. In this work, we first analyze such scale uncertainty both theoretically and practically. Then we perform scale recovery utilizing geometric constraint to estimate accurate scale factor, RANSAC(Random sample consensus) outlier removal is introduced into pipeline to obtain accurate ground point extraction. Adequate experiments on KITTI dataset(dataset generated by an autonomous driving platform built up by KIT and TRINA comprising stereo and optical flow image pairs as well as laser data, distributed to train set and test set on account of deep learning), show that, using only camera height prior, our proposed method, though not relying on additional sensors, is able to achieve accurate scale recovery and outperform existing scale recovery methods.
{"title":"Self-Supervised Monocular Depth Estimation Scale Recovery using RANSAC Outlier Removal","authors":"Zhuoyue Wu, G. Zhuo, Feng Xue","doi":"10.1109/CVCI51460.2020.9338538","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338538","url":null,"abstract":"Recently, self-supervised method has become an increasingly significant branch of depth estimation task, especially in the realm of autonomous driving applications. However, current per-pixel depth maps yielded from RGB images still suffer from uncertain scale factor generated by the nature of monocular image sequences, which further leads to the insufficiency in practical use. In this work, we first analyze such scale uncertainty both theoretically and practically. Then we perform scale recovery utilizing geometric constraint to estimate accurate scale factor, RANSAC(Random sample consensus) outlier removal is introduced into pipeline to obtain accurate ground point extraction. Adequate experiments on KITTI dataset(dataset generated by an autonomous driving platform built up by KIT and TRINA comprising stereo and optical flow image pairs as well as laser data, distributed to train set and test set on account of deep learning), show that, using only camera height prior, our proposed method, though not relying on additional sensors, is able to achieve accurate scale recovery and outperform existing scale recovery methods.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"186 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133227684","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/CVCI51460.2020.9338568
Liuhe, Xianzhong Zeng, Liu Shuo
With the development of electric power steering becoming more and more mature, many companies and universities have begun to develop steer-by-wire systems. In the online steering system, the steering wheel end is disconnected from the steering rack, and the road feel simulation unit is required to provide the driver with a realtime road feel to ensure the driver's maneuverability of the vehicle. There are many challenges in the generation of analog road feel. The basic functions include central position road feel, active return function, low-speed lightness and high-speed stability. The first part of this article discusses the control method of the road sense feedback motor, the second part is the design of the road sense feedback algorithm, and the third part is the test results and analysis. The motor control algorithm uses PID and feedforward algorithms to achieve precise current control. The road sense feedback part is designed to change the steering force gradient and damping with the speed of the vehicle. It is verified by Carsim and Matlab/Simulink joint simulation. The test part verifies the road sense algorithm. Adapt to different vehicle speeds and working conditions, and the smoothness of the center position and feel meets the needs of road feedback.
{"title":"Design of road feel feedback algorithm for steer-by- Wire","authors":"Liuhe, Xianzhong Zeng, Liu Shuo","doi":"10.1109/CVCI51460.2020.9338568","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338568","url":null,"abstract":"With the development of electric power steering becoming more and more mature, many companies and universities have begun to develop steer-by-wire systems. In the online steering system, the steering wheel end is disconnected from the steering rack, and the road feel simulation unit is required to provide the driver with a realtime road feel to ensure the driver's maneuverability of the vehicle. There are many challenges in the generation of analog road feel. The basic functions include central position road feel, active return function, low-speed lightness and high-speed stability. The first part of this article discusses the control method of the road sense feedback motor, the second part is the design of the road sense feedback algorithm, and the third part is the test results and analysis. The motor control algorithm uses PID and feedforward algorithms to achieve precise current control. The road sense feedback part is designed to change the steering force gradient and damping with the speed of the vehicle. It is verified by Carsim and Matlab/Simulink joint simulation. The test part verifies the road sense algorithm. Adapt to different vehicle speeds and working conditions, and the smoothness of the center position and feel meets the needs of road feedback.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129682347","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/CVCI51460.2020.9338655
Weining Ren, Kun Jiang, Xinxin Chen, Tuopu Wen, Diange Yang
The Visual-Inertial navigation system(VINS) has become a popular navigation approach in the field of unmanned aerial vehicles(UAV) or robotics. While its performance under autonomous driving scenario is not satisfactory due to the fact that autonomous driving scenario is more challenging and dynamic than the UAV scenario. Thus, the Visual-Inertial navigation system will collapse occasionally and thus undermine the navigation result. In this work, we propose a adaptive mechanism that could switch between three modes, only VINs, only GNSS and VINS&GNSS fusion. When Visual-Inertial component breaks down, our algorithm could only rely on the GNSS signal until VINS recovers. Similarly, when GNSS signal is not very accurate, our system could only rely on the VINS-Mono. We demonstrate our algorithm under challenging scenarios such as night sight and high speed road and do both qualitative analysis and quantitative analysis.
{"title":"Adaptive Sensor Fusion of Camera, GNSS and IMU for Autonomous Driving Navigation","authors":"Weining Ren, Kun Jiang, Xinxin Chen, Tuopu Wen, Diange Yang","doi":"10.1109/CVCI51460.2020.9338655","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338655","url":null,"abstract":"The Visual-Inertial navigation system(VINS) has become a popular navigation approach in the field of unmanned aerial vehicles(UAV) or robotics. While its performance under autonomous driving scenario is not satisfactory due to the fact that autonomous driving scenario is more challenging and dynamic than the UAV scenario. Thus, the Visual-Inertial navigation system will collapse occasionally and thus undermine the navigation result. In this work, we propose a adaptive mechanism that could switch between three modes, only VINs, only GNSS and VINS&GNSS fusion. When Visual-Inertial component breaks down, our algorithm could only rely on the GNSS signal until VINS recovers. Similarly, when GNSS signal is not very accurate, our system could only rely on the VINS-Mono. We demonstrate our algorithm under challenging scenarios such as night sight and high speed road and do both qualitative analysis and quantitative analysis.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114565469","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/CVCI51460.2020.9338661
Shuai Zhang, Guidong Yang, Yafei Wang, Qinghui Ji, Huimin Zhang
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.
{"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":"21 1","pages":"0"},"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}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338443
F. Zhao, Jianwei Zhang, Dongmei Wu, Guoqing Liu, Wensong Lang, Pinglai Wang
By analyzing the principle and method of the direct vector control torque control method for induction motor, an analysis method of the influence of motor parameter changes on the accuracy of motor electromagnetic torque control under steady-state conditions is proposed, and the application of four common flux observations is derived. In direct vector control, the error function of the electromagnetic torque is estimated. The influence of the change of induction motor parameters on the torque control accuracy is theoretically analyzed, and the simulation is verified by Simulink. The simulation proves that the method can correctly analyze the torque control accuracy under the condition of induction motor vector control without complicated calculations.
{"title":"The Effects of Parameter Variations on the Torque Control of Induction Motor","authors":"F. Zhao, Jianwei Zhang, Dongmei Wu, Guoqing Liu, Wensong Lang, Pinglai Wang","doi":"10.1109/CVCI51460.2020.9338443","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338443","url":null,"abstract":"By analyzing the principle and method of the direct vector control torque control method for induction motor, an analysis method of the influence of motor parameter changes on the accuracy of motor electromagnetic torque control under steady-state conditions is proposed, and the application of four common flux observations is derived. In direct vector control, the error function of the electromagnetic torque is estimated. The influence of the change of induction motor parameters on the torque control accuracy is theoretically analyzed, and the simulation is verified by Simulink. The simulation proves that the method can correctly analyze the torque control accuracy under the condition of induction motor vector control without complicated calculations.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124202315","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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