Human Drivers Based Active-Passive Model for Automated Lane Change

Lane change maneuver is a complicated maneuver, and incorrect maneuvering is an important reason for expressway accidents and fatalities. In this scenario, automated lane change has great potential to reduce the number of accidents. Previous research in this area, typically, focuses on the generation of an optimal lane change trajectory, while ignoring the human behavior model. To understand the human lane change behavior model, we carried out experiments on Japanese expressways. By analyzing the human-driver lane change data, we propose a two-segment lane change model that mimics the human-driver. We categorize the driving environment based on the observation grid and propose different lane change behaviors to handle the different scenarios. We develop an intuitive method to select the suitable lane change behavior, for a given scenario, using active (accelerate/decelerate) and passive (wait) information derived from the distance and related velocity (dx/dv) graph. Additionally, we also identify the most desirable and safe conditions for doing lane change based on the human driver preference data. We evaluated the proposed model by performing lane change simulations in the PreScan environment, while considering the vehicle motion/control model. The simulation results show the proposed model is able to handle complicated lane change scenarios with human driver-like performance.