Research on the classification for road traffic visibility based on the characteristics of driving behaviour–a driving simulator experiment

Purpose–The purpose of this paper is to performfine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions.Design/methodology/approach–A driving simulator experiment was conducted to collect data of speed and lane position.ANOVA was used to explore the difference in driving behavior under different visibility conditions.Findings–The results show that only average speed is significantly different under different visibility conditions.With the visibility reducing,the average vehicle speed decreases.The road visibility conditions in a straight segment can be divided intofive levels:less than 20,20-30,35-60,60-140 and more than 140 m.The road visibility conditions in a curve segment can be also divided into four levels:less than 20,20-30,35-60 and more than 60 m.Originality/value–Afine classification of road traffic visibility has been performed,and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.

Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

This paper presents a fusion control strategy of adaptive cruise control(ACC) and collision avoidance(CA),which takes into account a driver’s behavioral style. First, a questionnaire survey was performed to identify driver type, and the corresponding driving behavioral data were collected via driving simulator experiments, which served as the template data for the online identification of driver type. Then, the driveradaptive ACC/CA fusion control strategy was designed, and its effect was verified by virtual experiments. The results indicate that the proposed control strategy could achieve the fusion control of ACC and CA successfully and improve driver adaptability and comfort.

Behavioral adaptation of drivers when driving among automated vehicles

Purpose–This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles(AVs)compared to driving among manually driven vehicles(MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations.Here,mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.Design/methodology/approach–A driving simulator study is designed to explore whether such behavioral adaptations exist.Two different driving scenarios were explored on a three-lane highway:driving on the main highway and merging from an on-ramp.For this study,18 research participants were recruited.Findings–Behavioral adaptation can be observed in terms of car-following speed,car-following time gap,number of lane change and overall driving speed.The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs.Although significant differences in behavior were found in more than 90%of the research participants,they adapted their behavior differently,and thus,magnitude of the behavioral adaptation remains unclear.Originality/value–The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles.This finding differs from previous studies,which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles.Furthermore,the surrounding vehicles in this study are more“free flow’”compared to previous studies with a fixed formation such as platoons.Nevertheless,long-term observations are required to further support this claim.