Evaluation of Automatic Lane-Change Model Based on Vehicle Cluster Generalized Dynamic System

Abstract

The lane-change transportation research usually focuses on the efficiency and stability of the macro traffic flow while ignoring the driving comfort of individual vehicles. And many studies of lane-change models are often limited to the performance of a single vehicle, which leads to a lack of macroscopic evaluation. To solve the above limitations, an automatic lane-change generalized dynamic model is adopted. In this model, the lane-change behavior of an individual vehicle is considered as the generalized excitation and the restraining force between vehicles is described with the car-following model. Macro and micro evaluation indexes are also adopted to evaluate the automatic lane-change behavior in traffic flow. Furthermore, this paper proposes a modified intelligent driver model (IDM) to describe the state change process during lane change. The hyperbolic tangent transition function is used to eliminate the vehicle state mutation. The simulation results show that the proposed automatic lane-change generalized dynamic model can reflect the macro and micro parameters of the traffic flow. And compared with the traditional IDM model, the proposed HC-IDM model achieves higher comfort performance and lower fluctuation of traffic flow.