Realistic Data-driven Traffic Flow Animation using Texture Synthesis
IEEE Transactions on Visualization and Computer Graphics, 2018, 24(2): 1167-1178.
Qianwen Chao, Zhigang Deng, Jiaping Ren, Qianqian Ye, Xiaogang Jin
Examples of traffic flows synthesized by our approach. (Left) The synthesized traffic flows on a curvy road. (Middle) The synthesized traffic flows on a traffic-light controlled road. (Right) The synthesized traffic flows on an urban highway network.
Abstract
We present a novel
data-driven approach to populate virtual road networks with realistic
traffic flows. Specifically, given a limited set of vehicle trajectories
as the input samples, our approach first synthesizes a large set of
vehicle trajectories. By taking the spatio-temporal information of
traffic flows as a 2D texture, the generation of new traffic flows can
be formulated as a texture synthesis process, which is solved by
minimizing a newly developed traffic texture energy. The synthesized
output captures the spatio-temporal dynamics of the input traffic flows,
and the vehicle interactions in it strictly follow traffic rules. After
that, we position the synthesized vehicle trajectory data to virtual
road networks using a cage-based registration scheme, where a few
traffic-specific constraints are enforced to maintain each vehicle’s
original spatial location and synchronize its motion in concert with its
neighboring vehicles. Our approach is intuitive to control and scalable
to the complexity of virtual road networks. We validated our approach
through many experiments and paired comparison user studies.