The take-over control(ToC)of human–machine interaction is a hotspot.From automatic driving to manual driving,some factors affecting driver response time have not been considered in existing models,and little attentio...The take-over control(ToC)of human–machine interaction is a hotspot.From automatic driving to manual driving,some factors affecting driver response time have not been considered in existing models,and little attention has been paid to its effects on mixed traffic flow.This study establishes a ToC model of response based on adaptive control of thought-rational cognitive architecture(CAR-ToC)to investigate the effects of driver response time on traffic flow.A quantification method of driver’s situation cognition uncertainty is also proposed.This method can directly describe the cognitive effect of drivers with different cognitive characteristics on vehicle cluster situations.The results show that when driver response time in ToC is 4.2 s,the traffic state is the best.The greater the response time is,the more obvious the stop-and-go waves exhibit.Besides,crashes happen when manual vehicles hit other types of vehicles in ToC.Effects of driver response time on traffic are illustrated and verified from various aspects.Experiments are designed to verify that road efficiency and safety are increased by using a dynamic take-over strategy.Further,internal causes of effects are revealed and suggestions are discussed for the safety and efficiency of autonomous vehicles.展开更多
The objective of this paper is to incorporate vehicle mix in stimulus-response car-following models. Separate models were estimated for acceleration and deceleration responses to account for vehicle mix via both movem...The objective of this paper is to incorporate vehicle mix in stimulus-response car-following models. Separate models were estimated for acceleration and deceleration responses to account for vehicle mix via both movement state and vehicle type. For each model, three submodels were developed for different pairs of following vehicles including ＂automobile following automobile,＂ ＂automobile following truck,＂ and ＂truck following automobile.＂ The estimated model parameters were then validated against other data from a similar region and roadway. The results indicated that drivers＇ behaviors were significantly different among the different pairs of following vehicles. Also the magnitude of the estimated parameters depends on the type of vehicle being driven and/or followed. These results demonstrated the need to use separate models depending on movement state and vehicle type. The differences in parameter estimates confirmed in this paper highlight traffic safety and operational issues of mixed traffic operation on a single lane. The findings of this paper can assist transportation professionals to improve traffic simulation models used to evaluate the impact of different strategies on ameliorate safety and performance of highways. In addition, driver response time lag estimates can be used in roadway design to calculate important design parameters such as stopping sight distance on horizontal and vertical curves for both automobiles and trucks.展开更多
基金thankful to National Key Research and Development Plan(Grant No.2018YFB1600500)。
文摘The take-over control(ToC)of human–machine interaction is a hotspot.From automatic driving to manual driving,some factors affecting driver response time have not been considered in existing models,and little attention has been paid to its effects on mixed traffic flow.This study establishes a ToC model of response based on adaptive control of thought-rational cognitive architecture(CAR-ToC)to investigate the effects of driver response time on traffic flow.A quantification method of driver’s situation cognition uncertainty is also proposed.This method can directly describe the cognitive effect of drivers with different cognitive characteristics on vehicle cluster situations.The results show that when driver response time in ToC is 4.2 s,the traffic state is the best.The greater the response time is,the more obvious the stop-and-go waves exhibit.Besides,crashes happen when manual vehicles hit other types of vehicles in ToC.Effects of driver response time on traffic are illustrated and verified from various aspects.Experiments are designed to verify that road efficiency and safety are increased by using a dynamic take-over strategy.Further,internal causes of effects are revealed and suggestions are discussed for the safety and efficiency of autonomous vehicles.
文摘The objective of this paper is to incorporate vehicle mix in stimulus-response car-following models. Separate models were estimated for acceleration and deceleration responses to account for vehicle mix via both movement state and vehicle type. For each model, three submodels were developed for different pairs of following vehicles including ＂automobile following automobile,＂ ＂automobile following truck,＂ and ＂truck following automobile.＂ The estimated model parameters were then validated against other data from a similar region and roadway. The results indicated that drivers＇ behaviors were significantly different among the different pairs of following vehicles. Also the magnitude of the estimated parameters depends on the type of vehicle being driven and/or followed. These results demonstrated the need to use separate models depending on movement state and vehicle type. The differences in parameter estimates confirmed in this paper highlight traffic safety and operational issues of mixed traffic operation on a single lane. The findings of this paper can assist transportation professionals to improve traffic simulation models used to evaluate the impact of different strategies on ameliorate safety and performance of highways. In addition, driver response time lag estimates can be used in roadway design to calculate important design parameters such as stopping sight distance on horizontal and vertical curves for both automobiles and trucks.
