A Data-driven Model for Lane-changing in Traffic Simulation

Bi, Huikun; Mao, Tianlu; Wang, Zhaoqi; Deng, Zhigang

A Data-driven Model for Lane-changing in Traffic Simulation

Files

149-158.pdf (3.03 MB)

newdemo_0.mp4 (61.94 MB)

Date

2016

Authors

Bi, Huikun
Mao, Tianlu
Wang, Zhaoqi
Deng, Zhigang

Publisher

The Eurographics Association

Abstract

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.

        @inproceedings{10.2312:sca.20161232
,
booktitle = {Eurographics/ ACM SIGGRAPH Symposium on Computer Animation
},
editor = {Ladislav Kavan and Chris Wojtan
},
title = {{A Data-driven Model for Lane-changing in Traffic Simulation
}},
author = {Bi, Huikun and 
Mao, Tianlu and 
Wang, Zhaoqi and 
Deng, Zhigang
},
year = {2016
},
publisher = {The Eurographics Association
},
ISSN = {1727-5288
},
ISBN = {978-3-03868-009-3
},
DOI = {10.2312/sca.20161232
}
}