@article{Li2020, 
author = {Xuanpeng Li and Lifeng Zhu and Qifan Xue and Dong Wang and Yongjie Jessica Zhang},
title = {Fluid-inspired field representation for risk assessment in road scenes},
year = {2020},
journal = {Computational Visual Media},
volume = {6},
number = {4},
pages = {401-415},
keywords = {fluid-inspired risk field, multi-object tracking, road scenes},
url = {https://www.sciopen.com/article/10.1007/s41095-020-0190-8},
doi = {10.1007/s41095-020-0190-8},
abstract = {Prediction of the likely evolution of trafficscenes is a challenging task because of high uncertaintiesfrom sensing technology and the dynamic environment. It leads to failure of motion planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-inspired model to estimate collision risk in road scenes. Multi-object states are detected and tracked, and then a stable fluid model is adopted to construct the risk field. Objects’ state spaces are used as the boundary conditions in the simulation of advection and diffusion processes. We have evaluated our approach on the public KITTI dataset; our modelcan provide predictions in the cases of misdetection and tracking error caused by occlusion. It proves a promising approach for collision risk assessment in road scenes.}
}