scholarly journals Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures

Author(s):  
Allen Wang ◽  
Xin Huang ◽  
Ashkan Jasour ◽  
Brian Williams
2019 ◽  
Vol 127 ◽  
pp. 61-79 ◽  
Author(s):  
Christos Katrakazas ◽  
Mohammed Quddus ◽  
Wen-Hua Chen

2020 ◽  
Vol 6 (4) ◽  
pp. 401-415
Author(s):  
Xuanpeng Li ◽  
Lifeng Zhu ◽  
Qifan Xue ◽  
Dong Wang ◽  
Yongjie Jessica Zhang

AbstractPrediction 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.


2020 ◽  
Author(s):  
Swarn Singh Rathour ◽  
Tasuku Ishigooka ◽  
Satoshi Otsuka ◽  
RAUL MARTIN

2021 ◽  
Vol 122 ◽  
pp. 102820 ◽  
Author(s):  
Guofa Li ◽  
Yifan Yang ◽  
Tingru Zhang ◽  
Xingda Qu ◽  
Dongpu Cao ◽  
...  

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