It is a challenging problem in diesel engines to control throttle, variable geometry turbine(VGT)and exhaust gas recirculation (EGR). A effective method is model predictive control (MPC), which has been successfully applied to typical multi-input multi-output (MIMO) system with fast dynamics, actuator constraints, and strong couplings, such as diesel engines. In MPC controller design, the choice of output variables has a direct impact on the resulting control performance. Through investigating and discussing different selections of outputs, we propose that it is beneficial to select EGR-fraction and boost pressure as output variables while setting the oxygen fuel ratio as a constraint. Besides, equipping an integral action on the EGR ratio can improve the control performance.
{"title":"A Model Predictive Control Strategy with Integral Action on the Air Path System of a Diesel Engine","authors":"Jingyu Zhang, Jingfei Zhang, Xinda Yang, Pingyue Zhang","doi":"10.1109/CVCI51460.2020.9338638","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338638","url":null,"abstract":"It is a challenging problem in diesel engines to control throttle, variable geometry turbine(VGT)and exhaust gas recirculation (EGR). A effective method is model predictive control (MPC), which has been successfully applied to typical multi-input multi-output (MIMO) system with fast dynamics, actuator constraints, and strong couplings, such as diesel engines. In MPC controller design, the choice of output variables has a direct impact on the resulting control performance. Through investigating and discussing different selections of outputs, we propose that it is beneficial to select EGR-fraction and boost pressure as output variables while setting the oxygen fuel ratio as a constraint. Besides, equipping an integral action on the EGR ratio can improve the control 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":"130143959","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/CVCI51460.2020.9338470
Yuchen Liu, Haoyang Cheng, Zhiqiang Li
Intelligent driving functions, such as ACC (Adaptive Cruise Control) and ALC (Automated Lane Changes), require lane assignment for objects. It relies on an accurate traffic lane path estimation. This paper proposes a fused front lane trajectory estimation algorithm based on current common ADAS sensor configuration. This trajectory is generated by fusing information of lane markers, front object trails and host motion state. This algorithm uses a clothoid lane model and its coefficients is estimated by a Kalman Filter, which weighs predicted model state and current measurement. This approach is verified by a set of real road test data.
{"title":"Fused Front Lane Trajectory Estimation Based on Current ADAS Sensor Configuration","authors":"Yuchen Liu, Haoyang Cheng, Zhiqiang Li","doi":"10.1109/CVCI51460.2020.9338470","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338470","url":null,"abstract":"Intelligent driving functions, such as ACC (Adaptive Cruise Control) and ALC (Automated Lane Changes), require lane assignment for objects. It relies on an accurate traffic lane path estimation. This paper proposes a fused front lane trajectory estimation algorithm based on current common ADAS sensor configuration. This trajectory is generated by fusing information of lane markers, front object trails and host motion state. This algorithm uses a clothoid lane model and its coefficients is estimated by a Kalman Filter, which weighs predicted model state and current measurement. This approach is verified by a set of real road test data.","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":"130498435","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}
In human-machine cooperative decision making and control of intelligent vehicle, the intelligent system needs to understand driver's intention and desired vehicle trajectory in order to assist driver with safety driving in complex traffic scenes. In this paper, a vehicle trajectory prediction encoder-decoder model based on Gated Recurrent Unit (GRU) with attention mechanism is proposed. The proposed model is comprised of intention recognition module and trajectory prediction module. The intention recognition module was employed for recognizing driver's intention and calculating the probabilities of turning-left, lane-keeping, turning-right. The trajectory prediction module predicts vehicle trajectory using GRU decoder with attention mechanism, which takes vehicle historical position as input and predicts future position. Both intention recognition module and the trajectory prediction module share one encoder to save time. The NGSIM dataset was employed for training and testing. The experimental results indicate, comparing with traditional methods, the proposed horizontal-longitudinal decoupling hierarchical trajectory prediction method based on GRU neural network can predict driver's desired vehicle trajectory in a long prediction horizon and the attention mechanism improve the trajectory prediction accuracy at the same times.
{"title":"Attention -Based GRU for Driver Intention Recognition and Vehicle Trajectory Prediction","authors":"Zixu Hao, Xing Huang, Kaige Wang, Maoyuan Cui, Yantao Tian","doi":"10.1109/CVCI51460.2020.9338510","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338510","url":null,"abstract":"In human-machine cooperative decision making and control of intelligent vehicle, the intelligent system needs to understand driver's intention and desired vehicle trajectory in order to assist driver with safety driving in complex traffic scenes. In this paper, a vehicle trajectory prediction encoder-decoder model based on Gated Recurrent Unit (GRU) with attention mechanism is proposed. The proposed model is comprised of intention recognition module and trajectory prediction module. The intention recognition module was employed for recognizing driver's intention and calculating the probabilities of turning-left, lane-keeping, turning-right. The trajectory prediction module predicts vehicle trajectory using GRU decoder with attention mechanism, which takes vehicle historical position as input and predicts future position. Both intention recognition module and the trajectory prediction module share one encoder to save time. The NGSIM dataset was employed for training and testing. The experimental results indicate, comparing with traditional methods, the proposed horizontal-longitudinal decoupling hierarchical trajectory prediction method based on GRU neural network can predict driver's desired vehicle trajectory in a long prediction horizon and the attention mechanism improve the trajectory prediction accuracy at the same times.","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":"129441445","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/CVCI51460.2020.9338657
Hong-tao Hao, Cheng-xi Yang, Tao Han
A vehicle model equipped with wet dual clutch transmission based on Matlab/Simulink is established and the filling influence on the vehicle's gear shifting quality is simulated by the model. The simulation results show the friction work increases when the wet clutch is under fill and the jerk increases when the clutch is over-fill. So a two-parameter fuzzy control method is proposed to adjust the filling oil pressure and the built model is used to verify the effectiveness of the algorithm. Furthermore, rapid prototype simulation verification is carried out based on dSPACE hardware. The simulation results show that the adaptive control can reduce the jerk and friction work of the vehicle shifting process, and improve the shifting quality and driving comfort of the vehicle.
{"title":"Study on Adaptive Method of Filling for Wet Dual Clutch*","authors":"Hong-tao Hao, Cheng-xi Yang, Tao Han","doi":"10.1109/CVCI51460.2020.9338657","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338657","url":null,"abstract":"A vehicle model equipped with wet dual clutch transmission based on Matlab/Simulink is established and the filling influence on the vehicle's gear shifting quality is simulated by the model. The simulation results show the friction work increases when the wet clutch is under fill and the jerk increases when the clutch is over-fill. So a two-parameter fuzzy control method is proposed to adjust the filling oil pressure and the built model is used to verify the effectiveness of the algorithm. Furthermore, rapid prototype simulation verification is carried out based on dSPACE hardware. The simulation results show that the adaptive control can reduce the jerk and friction work of the vehicle shifting process, and improve the shifting quality and driving comfort of the 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":"128905501","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/CVCI51460.2020.9338520
W. Cai, Ganglei He, Jianlong Hu, Haiyan Zhao, Yuhai Wang, B. Gao
In this paper, an interactive intention prediction method is proposed. Firstly, the Hidden Markov Model integrated with Gaussian Mixture Model is modeled for current behavior recognition and its parameters are trained through NGSIM dataset. Then, a trajectory prediction method based on Frenet frame is used to predict the future traffic situation, considering which future behavior reasoning is realized by maximum expected utility theory. The final intention prediction result is a combination of historical trajectory recognition and future behavior reasoning. The simulation results show that the proposed method has the ability of reasonably reflecting the interaction process between vehicles and the prediction performance is good.
{"title":"A comprehensive intention prediction method considering vehicle interaction","authors":"W. Cai, Ganglei He, Jianlong Hu, Haiyan Zhao, Yuhai Wang, B. Gao","doi":"10.1109/CVCI51460.2020.9338520","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338520","url":null,"abstract":"In this paper, an interactive intention prediction method is proposed. Firstly, the Hidden Markov Model integrated with Gaussian Mixture Model is modeled for current behavior recognition and its parameters are trained through NGSIM dataset. Then, a trajectory prediction method based on Frenet frame is used to predict the future traffic situation, considering which future behavior reasoning is realized by maximum expected utility theory. The final intention prediction result is a combination of historical trajectory recognition and future behavior reasoning. The simulation results show that the proposed method has the ability of reasonably reflecting the interaction process between vehicles and the prediction performance is good.","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":"127587696","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/CVCI51460.2020.9338527
Yihang Guan, Hongliang Zhou, Zhen He, Zhiyuan Liu
This paper proposes a novel control strategy adjusting damper force of semi-active suspension to improve vehicle performance, including comfort performance considering both vertical vibration and roll motion during a gentle turn on uneven road, yaw tracking capability during a shaper turn, and rollover avoidance during a fierce turn. The coupled roll and yaw dynamics model and quarter suspension model are firstly established. Considering road unevenness is the main factor which causes vertical vibration and discomfort, a simple method to evaluate road unevenness with vertical acceleration of sprung mass is proposed. The coupled roll and yaw dynamics model is simplified to a prediction model with lower computational cost, and then an MPC controller is designed. Three different cost functions of comfort, yaw tracking and rollover avoidance respectively are designed, and their switching strategy is proposed according to priorities. Simulation results show that control strategy proposed in this paper is effective to reduce discomfort, overshoot of yaw rate and risk of rollover.
{"title":"Vehicle Safety and Comfort Control base on Semi-Active Suspension","authors":"Yihang Guan, Hongliang Zhou, Zhen He, Zhiyuan Liu","doi":"10.1109/CVCI51460.2020.9338527","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338527","url":null,"abstract":"This paper proposes a novel control strategy adjusting damper force of semi-active suspension to improve vehicle performance, including comfort performance considering both vertical vibration and roll motion during a gentle turn on uneven road, yaw tracking capability during a shaper turn, and rollover avoidance during a fierce turn. The coupled roll and yaw dynamics model and quarter suspension model are firstly established. Considering road unevenness is the main factor which causes vertical vibration and discomfort, a simple method to evaluate road unevenness with vertical acceleration of sprung mass is proposed. The coupled roll and yaw dynamics model is simplified to a prediction model with lower computational cost, and then an MPC controller is designed. Three different cost functions of comfort, yaw tracking and rollover avoidance respectively are designed, and their switching strategy is proposed according to priorities. Simulation results show that control strategy proposed in this paper is effective to reduce discomfort, overshoot of yaw rate and risk of rollover.","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":"124447132","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/CVCI51460.2020.9338628
Sen Yang, Junmin Wang, Junqiang Xi
Accurate vehicle speed prediction has important practical value to enhance fuel economy, drivability, and safety of intelligent vehicles. Current research on vehicle speed prediction mainly focuses on adapting to the dynamics, random and complex driving environment, while rarely takes drivers' driving preferences into account. In this paper, a learning-based prediction model consisted of an oriented Hidden Semi-Markov model (Oriented-HSMM) and an optimal preference speed prediction algorithm is proposed to leverage drivers' driving preferences into vehicle speed prediction. The Oriented-HSMM is developed to learn the spatial-temporal coherence of drivers' driving preference states under different traffic conditions and infer its long-term sequences in position domain. Based on these preference states, the optimal speed prediction algorithm using preference dynamics features is designed to retrieve the speed trajectory with maximal likelihood. To show its effectiveness, the proposed method is tested with the Next Generation Simulation (NGSIM) data on the US101 dataset comprising with the Hidden Markov model (HMM) and HSMM without considering driving preferences. Experiment results indicate that the proposed algorithm obtains the best performance with the mean absolute error (MAE) of 4.15 km/h and the root mean square error (RMSE) of 0.7603 km/h at 200 m prediction horizon.
{"title":"Leveraging Drivers' Driving Preferences into Vehicle Speed Prediction Using Oriented Hidden Semi-Markov model","authors":"Sen Yang, Junmin Wang, Junqiang Xi","doi":"10.1109/CVCI51460.2020.9338628","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338628","url":null,"abstract":"Accurate vehicle speed prediction has important practical value to enhance fuel economy, drivability, and safety of intelligent vehicles. Current research on vehicle speed prediction mainly focuses on adapting to the dynamics, random and complex driving environment, while rarely takes drivers' driving preferences into account. In this paper, a learning-based prediction model consisted of an oriented Hidden Semi-Markov model (Oriented-HSMM) and an optimal preference speed prediction algorithm is proposed to leverage drivers' driving preferences into vehicle speed prediction. The Oriented-HSMM is developed to learn the spatial-temporal coherence of drivers' driving preference states under different traffic conditions and infer its long-term sequences in position domain. Based on these preference states, the optimal speed prediction algorithm using preference dynamics features is designed to retrieve the speed trajectory with maximal likelihood. To show its effectiveness, the proposed method is tested with the Next Generation Simulation (NGSIM) data on the US101 dataset comprising with the Hidden Markov model (HMM) and HSMM without considering driving preferences. Experiment results indicate that the proposed algorithm obtains the best performance with the mean absolute error (MAE) of 4.15 km/h and the root mean square error (RMSE) of 0.7603 km/h at 200 m prediction horizon.","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":"124562340","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/CVCI51460.2020.9338515
Guanming Liu, Bin Xiao, Daofei Li
Traffic complexities in no-signal intersections lead to amounts of accidents, among of which are due to inappropriate decision based on inconsiderate judgements of the other traffic users. Focusing on an example intersection driving scenario, this paper analyses the decision-making behaviour of two crossing vehicles at intersections without traffic lights, while considering the influence of safety factor, traffic efficiency and drivers' irrationality, etc. We propose a corresponding utility model to treat the whole dynamic process as finite repeated games. Nash Equilibrium approach is adopted to solve the decision-making problem at intersections. The effectiveness of the proposed decision algorithm is validated by both simulation and human-in-the-loop experiments.
{"title":"Game-theory Based Driving Decision Algorithm for Intersection Scenarios Considering Driver Irrationality","authors":"Guanming Liu, Bin Xiao, Daofei Li","doi":"10.1109/CVCI51460.2020.9338515","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338515","url":null,"abstract":"Traffic complexities in no-signal intersections lead to amounts of accidents, among of which are due to inappropriate decision based on inconsiderate judgements of the other traffic users. Focusing on an example intersection driving scenario, this paper analyses the decision-making behaviour of two crossing vehicles at intersections without traffic lights, while considering the influence of safety factor, traffic efficiency and drivers' irrationality, etc. We propose a corresponding utility model to treat the whole dynamic process as finite repeated games. Nash Equilibrium approach is adopted to solve the decision-making problem at intersections. The effectiveness of the proposed decision algorithm is validated by both simulation and human-in-the-loop experiments.","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":"115742711","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/CVCI51460.2020.9338654
Xu Jingyi, Zhang Tao, L. Junjie, Zhang Yang, Ge Pingshu, Yang Jingjing
Aiming at the functions and characteristics of the EPS power steering system, power model of electric power steering is established, the ideal power-assisted characteristic curve is analyzed, the EPS steering system test is designed based on the bench, and the driving simulator hardware-in-loop test is carried out. The effects of EPS power steering in stationary and motion state are tested. The results show that the experimental results are accord with the ideal assist characteristic curve, and the designed driving simulator hardware-in-loop EPS experiment has a good assist effect, which is beneficial to improve the operating sensitivity and steering stability of electric vehicles.
{"title":"Research on EPS Assist Characteristics Based on Hardware-in-loop Simulation","authors":"Xu Jingyi, Zhang Tao, L. Junjie, Zhang Yang, Ge Pingshu, Yang Jingjing","doi":"10.1109/CVCI51460.2020.9338654","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338654","url":null,"abstract":"Aiming at the functions and characteristics of the EPS power steering system, power model of electric power steering is established, the ideal power-assisted characteristic curve is analyzed, the EPS steering system test is designed based on the bench, and the driving simulator hardware-in-loop test is carried out. The effects of EPS power steering in stationary and motion state are tested. The results show that the experimental results are accord with the ideal assist characteristic curve, and the designed driving simulator hardware-in-loop EPS experiment has a good assist effect, which is beneficial to improve the operating sensitivity and steering stability of electric 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":"132529633","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/CVCI51460.2020.9338477
Liu Yingzhe, Ma Wenlun, Wang Li, Fan Jingjing
Unmanned tracked vehicle mostly use remote control. Communication delay and unclear images are easy to cause operator errors in operation, especially when starting on large uphill, safety accidents such as slipping and sideslip often occur. Aiming at the problem of uphill assist safety, on the basis of analyzing the longitudinal dynamics of the vehicle, a feedforward and feedback control method is proposed, the evaluation index of the big uphill assist performance is designed, the target driving force is obtained according to the uphill resistance and braking force, and the feedforward is designed The compensator calculates the feedforward driving force through the braking force, and then completes the feedback closed-loop control through the quasi-sliding mode controller. Through model simulation, this design method can help the unmanned tracked vehicle to start safely on the uphill, and the vehicle speed tracking effect is good, reducing the driver's control difficulty in uphill starting, and meeting the design requirements of the unmanned tracked vehicle's safe starting control on the uphill.
{"title":"Unmanned Tracked Vehicle Uphill Assist Control Method Based on Quasi-sliding Mode Control","authors":"Liu Yingzhe, Ma Wenlun, Wang Li, Fan Jingjing","doi":"10.1109/CVCI51460.2020.9338477","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338477","url":null,"abstract":"Unmanned tracked vehicle mostly use remote control. Communication delay and unclear images are easy to cause operator errors in operation, especially when starting on large uphill, safety accidents such as slipping and sideslip often occur. Aiming at the problem of uphill assist safety, on the basis of analyzing the longitudinal dynamics of the vehicle, a feedforward and feedback control method is proposed, the evaluation index of the big uphill assist performance is designed, the target driving force is obtained according to the uphill resistance and braking force, and the feedforward is designed The compensator calculates the feedforward driving force through the braking force, and then completes the feedback closed-loop control through the quasi-sliding mode controller. Through model simulation, this design method can help the unmanned tracked vehicle to start safely on the uphill, and the vehicle speed tracking effect is good, reducing the driver's control difficulty in uphill starting, and meeting the design requirements of the unmanned tracked vehicle's safe starting control on the uphill.","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":"125371506","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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