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Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles 被引量:8
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作者 YI Zi-wei LU Wen-qi +2 位作者 XU Ling-hui QU Xu RAN Bin 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第11期3499-3512,共14页
The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circu... The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item. 展开更多
关键词 linear stability intelligent driver model connected and automated vehicles
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Simulation of automobile fuel consumption and emissions for various driver's manual shifting habits 被引量:1
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作者 B.Mashadi Y.Amiri-rad +1 位作者 A.Afkar M.Mahmoodi-kaleybar 《Journal of Central South University》 SCIE EI CAS 2014年第3期1058-1066,共9页
The effects of different habits of the drivers on gear shifting strategies for manual powertrain were investigated. For the realization of simulation, the shifting habits of the drivers were conducted in the Advisor s... The effects of different habits of the drivers on gear shifting strategies for manual powertrain were investigated. For the realization of simulation, the shifting habits of the drivers were conducted in the Advisor software to investigate and compare the emission rates. Simulation was developed based on the optimal gear shifting strategy and criteria and was validated both in fuel economy and emissions by analyzing the results in the various driving cycle and driving styles. To explore an optimal gear shifting strategy with best fuel economy and lowest emission for a manual transmission, a strategy was designed with a highest possible gear criterion as long as the torque requirement can be satisfied. Based on two different criteria, namely the engine working conditions and the driver's intention, the governing parameters in decision making for gear shifting of manual transmission in conventional engine were discussed. It is also shown that the optimum gear shifting strategy is based on that both the engine state and the driver's intention eliminates unnecessary shiftings that are present when the intention is overlooked. The optimum shifting habit and the best driving cycle in terms of minimum emissions and fuel consumption were proposed. 展开更多
关键词 gear shifting strategies driving cycle driver model ADVISOR
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Different human-simulated characteristics of the driver models in the forward simulation of hybrid electric vehicles
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作者 王伟达 项昌乐 +1 位作者 刘辉 韩立金 《Journal of Beijing Institute of Technology》 EI CAS 2011年第2期192-198,共7页
Based on the control theories of PID, fuzzy logic and expert PID, the driver models are built and applied in the forward simulation for hybrid electric vehicles (HEV). The impact to the vehicle speed tracking and th... Based on the control theories of PID, fuzzy logic and expert PID, the driver models are built and applied in the forward simulation for hybrid electric vehicles (HEV). The impact to the vehicle speed tracking and the fuel economy is compared among the different driver models. The different human-simulated characteristics of the driver models are emphatically analyzed. The analysis results indicate that the driver models based on PID, simple fuzzy logic and expert PID are corresponding to the handling characteristics of different drives. The driver models of different human-simulated characteristics bring the handling divergence of drivers with different driving level and habit to the HEV forward simulation, and that is significant to the all-around verification and validation of the control strategy for HEV. System simulation results of different driver models validate the impact of driver models to the dynamic and fuel economy performance of HEV. 展开更多
关键词 hybrid electric vehicles(HEV) driver model human-simulated characteristic forward simulation
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An extended smart driver model considering electronic throttle angle changes with memory
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作者 Congzhi Wu Hongxia Ge Rongjun Cheng 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第1期192-198,共7页
Based on the fact that the electronic throttle angle effect performs well in the traditional car following model,this paper attempts to introduce the electronic throttle angle into the smart driver model(SDM)as an acc... Based on the fact that the electronic throttle angle effect performs well in the traditional car following model,this paper attempts to introduce the electronic throttle angle into the smart driver model(SDM)as an acceleration feedback control term,and establish an extended smart driver model considering electronic throttle angle changes with memory(ETSDM).In order to show the practicability of the extended model,the next generation simulation(NGSIM)data was used to calibrate and evaluate the extended model and the smart driver model.The calibration results show that,compared with SDM,the simulation value based on the ETSDM is better fitted with the measured data,that is,the extended model can describe the actual traffic situation more accurately.Then,the linear stability analysis of ETSDM was carried out theoretically,and the stability condition was derived.In addition,numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow.The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow,which shows the feasibility and superiority of the proposed model to a certain extent. 展开更多
关键词 smart driver model(SDM) electronic throttle angle changes with memory(ETSDM) parameter calibration stability analysis
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Uniform wire segmentation algorithm of distributed interconnects
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作者 尹国丽 Lin Zhenghui 《High Technology Letters》 EI CAS 2007年第2期198-202,共5页
A uniform wire segmentation algorithm for performance optimization of distributed RLC interconnects was proposed in this paper. The optimal wire length for identical segments and buffer size for buffer inser-tion are ... A uniform wire segmentation algorithm for performance optimization of distributed RLC interconnects was proposed in this paper. The optimal wire length for identical segments and buffer size for buffer inser-tion are obtained through computation and derivation, based on a 2-pole approximatian model of distribut-ed RLC interconnect. For typical inductance value and long wires under 180nm technology, experiments show that the uniform wire segmentation technique proposed in the paper can reduce delay by about 27%~56%, while requires 34%~69% less total buffer usage and thus 29% to 58% less power consump-tion. It is suitable for long RLC interconnect performance optimization. 展开更多
关键词 distributed RLC interconnect uniform wire segmentation buffer insertion driver modeling
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Parallel Distributed Compensation/H∞Control of Lane‑keeping System Based on the Takagi‑Sugeno Fuzzy Model 被引量:3
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作者 Wuwei Chen Linfeng Zhao +1 位作者 Huiran Wang Yangcheng Huang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2020年第4期126-138,共13页
Current research on lane-keeping systems ignores the effect of the driver and external resistance on the accuracy of tracking the lane centerline.To reduce the lateral deviation of the vehicle,a lane-keeping control m... Current research on lane-keeping systems ignores the effect of the driver and external resistance on the accuracy of tracking the lane centerline.To reduce the lateral deviation of the vehicle,a lane-keeping control method based on the fuzzy Takagi-Sugeno(T-S)model is proposed.The method adopts a driver model based on near and far visual angles,and a driver-road-vehicle closed-loop model based on longitudinal nonlinear velocity variation,obtaining the expected assist torque with a robust H∞controller which is designed based on parallel distributed compensation and linear matrix inequality.Considering the external influences of tire adhesion and aligning torque when the vehicle is steering,a feedforward compensation control is designed.The electric power steering system is adopted as the actuator for lane-keeping,and active steering redressing is realized by a control motor.Simulation results based on Carsim/Simulink and real vehicle test results demonstrate that the method helps to maintain the vehicle in the lane centerline and ensures driving safety. 展开更多
关键词 driver model Lane-keeping system T-S fuzzy model H∞controller Feedforward compensation control
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Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles
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作者 Gergo Ferenc Igneczi Erno Horvath +1 位作者 Roland Toth Krisztian Nyilas 《Automotive Innovation》 EI CSCD 2024年第1期59-70,共12页
Automated driving systems are often used for lane keeping tasks.By these systems,a local path is planned ahead of the vehicle.However,these paths are often found unnatural by human drivers.In response to this,this pap... Automated driving systems are often used for lane keeping tasks.By these systems,a local path is planned ahead of the vehicle.However,these paths are often found unnatural by human drivers.In response to this,this paper proposes a linear driver model,which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving.The model input is the road curvature,effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm.A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model,demonstrating its capacity to emulate the average behavioral pat-terns observed in human curve path selection.Statistical analyses further underscore the model's robustness,affirming the authenticity of the established relationships.This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences. 展开更多
关键词 Naturalistic driving Identification driver models Path planning
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Characterizing human driver characteristics using an artificial neural network and a theoretical model
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作者 Sangmyoeng Kim Takeshi Miyamoto +1 位作者 Tatsuya Kuboyama Yasuo Moriyoshi 《Control Theory and Technology》 EI CSCD 2022年第2期263-278,共16页
Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with differ... Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with different experiences often yield similar results under the same driving conditions.If the features of human drivers are known,the control inputs to each driver,including warnings,will be customized to optimize each man–machine vehicle system.Therefore,it is crucial to determine how to characterize human drivers quantitatively.This study proposes a method to estimate the parameters of a theoretical model of human drivers.The method uses an artificial neural network(ANN)model and a numerical procedure to interpret the identified ANN models theoretically.Our approach involves the following process.First,we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle.Subsequently,we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver.Specifically,we simulate the driver’s behaviors using the identified ANN models with controlled inputs.Finally,we estimate the theoretical driver model parameters using the numerical simulation results.A proportional-integral-differential(PID)control model is used as the theoretical model.The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers.Moreover,the results suggest that vehicular factors influence the parameter combinations of human drivers. 展开更多
关键词 Neural network IDENTIFICATION driver model Chassis dynamometer driver characteristics
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Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model 被引量:15
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作者 Lei HE Chang-fu ZONG Chang WANG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2012年第3期208-217,共10页
We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM r... We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance. 展开更多
关键词 Vehicle engineering Driving intention recognition Driving behaviour prediction driver model Double-layer hidden Markov model (HMM)
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Steering control strategy guide by two preview vision cues 被引量:7
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作者 SHEN Huan LING Rui +1 位作者 MAO JianGuo LI ShunMing 《Science China(Technological Sciences)》 SCIE EI CAS 2012年第9期2662-2670,共9页
Vision cues play an important role in states feedback in motion control.However,the existing driver steering models consider little about vision cues utilized by human drivers during their steering procedure.This pape... Vision cues play an important role in states feedback in motion control.However,the existing driver steering models consider little about vision cues utilized by human drivers during their steering procedure.This paper presents a novel steering control strategy based on two preview points(far point and near point).The far point is used to compensate the steering wheel by predicting the upcoming curvature change with respect to the lane,while the near point as vision feedback,which is used to tune the steering wheel by estimating the errors of vehicle states and lane center.To obtain much smoother lateral acceleration during steering,a forward internal model is established using a second-order yaw dynamics system that captures the influence of yaw angular acceleration caused by steering wheel angle.The input parameter of the second-order system is the vision cues of both the near and far points,and the output parameters are the ideal yaw angle and yaw rate.To calculate suitable the steering wheel angle,an adaptive controller is designed using fuzzy sliding technology,which is used as the input of the vehicle system dynamics.Numerical simulation results show that the proposed method performs better than the existing driver steering models in case of imitating human drivers' behavior,and exhibits excellent adaption to the lane curvature change. 展开更多
关键词 autonomous driving lateral control driver model fuzzy logic cognitive vehicle
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Optimal control methods in intelligent vehicles
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作者 Hong Chen Lulu Guo +2 位作者 Ting Qu Bingzhao Gao Fei Wang 《Journal of Control and Decision》 EI 2017年第1期32-56,共25页
In recent years,intelligent vehicles(IVs)have become a hot spot in automotive industry.Key technologies of IVs range over the field of sensing,decision-making and control.Among them,control technology provides an enab... In recent years,intelligent vehicles(IVs)have become a hot spot in automotive industry.Key technologies of IVs range over the field of sensing,decision-making and control.Among them,control technology provides an enabling support for improving autonomous driving safety,reducing energy consumption and carbon emission.This paper focuses on some aspects of applying advanced control methodologies in IVs through several selected examples including eco-driving and MPC-based driver modelling. 展开更多
关键词 Control technology intelligent vehicles eco-driving MPC-based driver modelling
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Planning and Real-time Pricing of EV Charging Stations Considering Social Welfare and Profitability Balance
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作者 Suyang Zhou Yuxuan Zhuang +4 位作者 Zhi Wu Wei Gu Peng Yu Jinqiao Du Xiner Luo 《CSEE Journal of Power and Energy Systems》 SCIE CSCD 2021年第6期1289-1301,共13页
This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station... This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%. 展开更多
关键词 Bi-level model EV charging station planning particle swarm optimization real-time pricing drivers’preference model logit model
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