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Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning. (arXiv:2205.03212v1 [cs.CV])
May 9, 2022, 1:10 a.m. | Khushdeep Singh Mann, Abhishek Tomy, Anshul Paigwar, Alessandro Renzaglia, Christian Laugier
cs.CV updates on arXiv.org arxiv.org
Reliably predicting future occupancy of highly dynamic urban environments is
an important precursor for safe autonomous navigation. Common challenges in the
prediction include forecasting the relative position of other vehicles,
modelling the dynamics of vehicles subjected to different traffic conditions,
and vanishing surrounding objects. To tackle these challenges, we propose a
spatio-temporal prediction network pipeline that takes the past information
from the environment and semantic labels separately for generating future
occupancy predictions. Compared to the current SOTA, our approach predicts …
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