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Research Article | Open Access

Fluid-inspired field representation for risk assessment in road scenes

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Department of Mechanical Engineering, CarnegieMellon University, Pittsburgh, PA 15213, USA
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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.

Keywords

fluid-inspired risk fieldmulti-object trackingroad scenes

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Computational Visual Media
Volume 6 Issue 4,
December 2020
Pages 401-415
DOI: 10.1007/s41095-020-0190-8

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Cite this article:
Li X, Zhu L, Xue Q, et al. Fluid-inspired field representation for risk assessment in road scenes. Computational Visual Media, 2020, 6(4): 401-415. https://doi.org/10.1007/s41095-020-0190-8

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Received: 01 June 2020
Accepted: 21 July 2020
Published: 29 October 2020
© The Author(s) 2020

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