Lane change behavior with uncertainty and fuzziness for human driving vehicles and its simulation in mixed traffic

Abstract

In the process of constructing the driver’s lane-changing model, in addition to the dynamic states of the surrounding vehicles affecting the driver’s lane-changing decision, various traffic environment factors and the driver’s own personality also affect the driver’s lane-changing probability. At the same time, these influencing factors also make the lane changing process ambiguous and uncertain. To address these issues, in this study the membership function of soft set is applied to quantify the impact of some typical ambiguous factors and relationships, such as weather conditions, driver personality, and vehicle type, on the driver’s driving process. The mapping relationships between these influencing factors and the driver’s expected speed and aggression are obtained through decision rules. Then a new lane changing model considering uncertain or ambiguous factors and relationships is proposed. On the basis of this model, simulation experiments of mixed traffic flow under the conditions of 0%, 20%, 40% and 60% penetration rate of autonomous vehicles were carried out. The experimental results reasonably present the impact of autonomous vehicle penetration and some ambiguity on the lance change behavior.