Prediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from sensing technology and the dynamic environment.It leads to failure of motion planning for intelligent agent...Prediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from 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 model can 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.展开更多
基金the National Natural Science Foundation of China under Grant No.61906038the Fundamental Research Funds for the Central Universities under Grant No.2242019K40039the Zhishan Youth Scholar Program of Southeast University。
文摘Prediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from 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 model can 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.
