A data-driven model for lane-changing in traffic simulation

In this paper, we propose a new data-driven model to simulate the process of lane-changing in traffic simulation. Specifically, we first extract the features from surrounding vehicles that are relevant to the lane-changing of the subject vehicle. Then, we learn the lane-changing characteristics from the ground-truth vehicle trajectory data using randomized forest and back-propagation neural network algorithms. Our method can make the subject vehicle to take account of more gap options on the target lane to cut in as well as achieve more realistic lane-changing trajectories for the subject vehicle and the follower vehicle. Through many experiments and comparisons with selected state-of-the-art methods, we demonstrate that our approach can soundly outperform them in terms of the accuracy and quality of lane-changing simulation. Our model can be flexibly used together with a variety of existing car-following models to produce natural traffic animations in various virtual environments.