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
In order to reflect the influence of the drivers＇ characteristic differences on intersection capacity under a mixed traffic flow, a driver correction coefficient for the intersection capacity calculation according to...In order to reflect the influence of the drivers＇ characteristic differences on intersection capacity under a mixed traffic flow, a driver correction coefficient for the intersection capacity calculation according to the driver＇s visual characteristics is proposed. First, the parameters of the driver＇s visual characteristics at some real roads, including gaze fixation distribution, mean fixation duration, visual angle distribution and some other parameters at intersections, are collected. Then, the relationship between the traffic flow rate at intersections and the parameters of driver eye movements are established. The analytical results indicate that when the traffic flow is unsaturated, the parameters of driver eye movements change relatively little; however, when the traffic flow is saturated, the parameters of driver eye movements change drastically. Finally, the saturation-flow-rate model is modified according to the parameters of driver eye movements; thus, a capacity model of intersections considering the driver＇s visual characteristics is obtained.展开更多
The driver’s characteristics(e.g.,timid and aggressive)has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogene...The driver’s characteristics(e.g.,timid and aggressive)has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous.In the real traffic environment,the drivers are distinct due to a variety of factors such as personality characteristics.To better reflect the reality,we introduce the penetration rate to describe the degree of drivers’heterogeneity(i.e.,timid and aggressive),and proceed to propose a generalized heterogeneous car-following model with different driver’s characteristics.Through the linear stability analysis,the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method.The impacts of the penetration rate of drivers with low intensity,the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation.Results show that higher penetration of aggressive drivers contributes to traffic flow stability.The average value has a great impact on the stability of traffic flow,whereas the impact of the standard deviation is trivial.展开更多
The purpose of this study is to clarify the driving behavior and reaction pattern peculiar to the elderly. In this paper, we conducted a brake operation experiment with five young people, and aimed to create an evalua...The purpose of this study is to clarify the driving behavior and reaction pattern peculiar to the elderly. In this paper, we conducted a brake operation experiment with five young people, and aimed to create an evaluation index and an experimental method that can extract the characteristics of emergency brake operation from pedaling force and lower limb movement. The results showed that in order to operate the brakes strongly and quickly, the knee was firmly flection, and then the pedaling force was increased when the knee was extended. Furthermore, it was shown that if the brakes were operated without moving the ankle joint, the operation would be quicker.展开更多
To promote the intelligent vehicle safety and reduce the driver steering workload,stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteris...To promote the intelligent vehicle safety and reduce the driver steering workload,stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account.First,a shared steering control framework with adjustable driving weight is proposed,and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory.Moreover,the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached.Finally,simulation and virtual driving tests are carried out to verify the superiority of the proposed method.The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention,and it can achieve better auxiliary effect with less control cost.In addition,the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.展开更多
Purpose–The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.Design/methodology/approach–A total of 1,432 crashes and 19,71...Purpose–The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.Design/methodology/approach–A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research.The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage.The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics(age,driving experience or their combination)and the crash risk regarding cell phone engagements,as well as the correlation among the likelihood of the cell phone engagement during the driving,multiple driver demographic characteristics(gender,age and driving experience)and environment conditions.Findings–Senior drivers face an extremely high crash risk when distracted by cell phone during driving,but they are not involved in crashes at a large scale.On the contrary,cell phone usages account for a far larger percentage of total crashes for young drivers.Similarly,experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving,and cell phone engagements are attributed to a lower percentage of total crashes for them.Furthermore,experienced,senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving.Originality/value–The results provide support to guide countermeasures and vehicle design.展开更多
Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surroundin...Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surrounding landscapes)can automatically guide drivers to choose appropriate speeds on different road categories.This study proposes an improved lightweight convolutional neural network(LW-CNN)that includes drivers’visual perception characteristics(i.e.,depth perception and dynamic vision)to conduct the self-explaining analysis of the facility environment on 2-lane rural roads.Data for this study are gathered through naturalistic driving experiments on 2-lane rural roads across five Chinese provinces.A total of 3502 visual facility environment images,alongside their corresponding operation speeds and speed limits,are collected.The improved LW-CNN exhibits high accuracy and efficiency in predicting operation speeds with these visual facility environment images,achieving a train loss of 0.05%and a validation loss of 0.15%.The semantics of facility environments affecting operation speeds are further identified by combining this LW-CNN with the gradient-weighted class activation mapping(Grad-CAM)algorithm and the semantic segmentation network.Then,six typical 2-lane rural road categories perceived by drivers with different operation speeds and speeding probability(SP)are sum-marized using k-means clustering.An objective and comprehensive analysis of each category’s semantic composition and depth features is conducted to evaluate their influence on drivers’speeding probability and road category perception.The findings of this study can be directly used to optimize facility environments from drivers’visual perception to decrease speeding-related crashes.展开更多
文摘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.
基金The National Natural Science Foundation of China (No.50708019)Huo Yingdong Education Foundation(No.104010)Jiangsu Qing Lan Project
文摘In order to reflect the influence of the drivers＇ characteristic differences on intersection capacity under a mixed traffic flow, a driver correction coefficient for the intersection capacity calculation according to the driver＇s visual characteristics is proposed. First, the parameters of the driver＇s visual characteristics at some real roads, including gaze fixation distribution, mean fixation duration, visual angle distribution and some other parameters at intersections, are collected. Then, the relationship between the traffic flow rate at intersections and the parameters of driver eye movements are established. The analytical results indicate that when the traffic flow is unsaturated, the parameters of driver eye movements change relatively little; however, when the traffic flow is saturated, the parameters of driver eye movements change drastically. Finally, the saturation-flow-rate model is modified according to the parameters of driver eye movements; thus, a capacity model of intersections considering the driver＇s visual characteristics is obtained.
基金supported by the Regional Joint Fund for Foundation and Applied Research Fund of Guangdong Province,China(Grant No.2019A1515111200)Youth Innovation Talents Funds of Colleges and Universities in Guangdong Province,China(Grant No.2018KQNCX287)+2 种基金the Science and Technology Program of Guangzhou,China(Grant No.201904010202)the National Science Foundation of China(Grant No.72071079)the Science and Technology Program of Guangdong Province,China(Grant No.2020A1414010010).
文摘The driver’s characteristics(e.g.,timid and aggressive)has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous.In the real traffic environment,the drivers are distinct due to a variety of factors such as personality characteristics.To better reflect the reality,we introduce the penetration rate to describe the degree of drivers’heterogeneity(i.e.,timid and aggressive),and proceed to propose a generalized heterogeneous car-following model with different driver’s characteristics.Through the linear stability analysis,the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method.The impacts of the penetration rate of drivers with low intensity,the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation.Results show that higher penetration of aggressive drivers contributes to traffic flow stability.The average value has a great impact on the stability of traffic flow,whereas the impact of the standard deviation is trivial.
文摘The purpose of this study is to clarify the driving behavior and reaction pattern peculiar to the elderly. In this paper, we conducted a brake operation experiment with five young people, and aimed to create an evaluation index and an experimental method that can extract the characteristics of emergency brake operation from pedaling force and lower limb movement. The results showed that in order to operate the brakes strongly and quickly, the knee was firmly flection, and then the pedaling force was increased when the knee was extended. Furthermore, it was shown that if the brakes were operated without moving the ankle joint, the operation would be quicker.
基金National Nature Science Foundation of China(62103162,U19A2069 and 61790563)Scientific and Technological Innovation 2030"NewGeneration Artificial Intelligence"Major Project(2020AAA0108105).
文摘To promote the intelligent vehicle safety and reduce the driver steering workload,stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account.First,a shared steering control framework with adjustable driving weight is proposed,and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory.Moreover,the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached.Finally,simulation and virtual driving tests are carried out to verify the superiority of the proposed method.The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention,and it can achieve better auxiliary effect with less control cost.In addition,the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.
基金supported in part by the Joint Laboratory for Internet of Vehicles,Ministry of Education-China Mobile Communications Corporation under Grant ICV-KF2018-01in part by the National Natural Science Foundation of China underGrant 51975194 and 51905161.
文摘Purpose–The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.Design/methodology/approach–A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research.The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage.The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics(age,driving experience or their combination)and the crash risk regarding cell phone engagements,as well as the correlation among the likelihood of the cell phone engagement during the driving,multiple driver demographic characteristics(gender,age and driving experience)and environment conditions.Findings–Senior drivers face an extremely high crash risk when distracted by cell phone during driving,but they are not involved in crashes at a large scale.On the contrary,cell phone usages account for a far larger percentage of total crashes for young drivers.Similarly,experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving,and cell phone engagements are attributed to a lower percentage of total crashes for them.Furthermore,experienced,senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving.Originality/value–The results provide support to guide countermeasures and vehicle design.
基金supported by the National Natural Science Foundation of China(No.52102416)the Natural Science Foundation of Shanghai(No.22ZR1466000)the Fundamental Research Funds for the Central Universities of China(No.22120240159).
文摘Speeding is one of the primary contributors to rural road crashes.Self-explaining theory offers a solution to reduce speeding,which suggests that well-designed facility environments(i.e.,road facilities and surrounding landscapes)can automatically guide drivers to choose appropriate speeds on different road categories.This study proposes an improved lightweight convolutional neural network(LW-CNN)that includes drivers’visual perception characteristics(i.e.,depth perception and dynamic vision)to conduct the self-explaining analysis of the facility environment on 2-lane rural roads.Data for this study are gathered through naturalistic driving experiments on 2-lane rural roads across five Chinese provinces.A total of 3502 visual facility environment images,alongside their corresponding operation speeds and speed limits,are collected.The improved LW-CNN exhibits high accuracy and efficiency in predicting operation speeds with these visual facility environment images,achieving a train loss of 0.05%and a validation loss of 0.15%.The semantics of facility environments affecting operation speeds are further identified by combining this LW-CNN with the gradient-weighted class activation mapping(Grad-CAM)algorithm and the semantic segmentation network.Then,six typical 2-lane rural road categories perceived by drivers with different operation speeds and speeding probability(SP)are sum-marized using k-means clustering.An objective and comprehensive analysis of each category’s semantic composition and depth features is conducted to evaluate their influence on drivers’speeding probability and road category perception.The findings of this study can be directly used to optimize facility environments from drivers’visual perception to decrease speeding-related crashes.
