動的環境における時空間占有予測のための双叉型ConvLSTM
Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environments
静的環境の観測変化と動的物体の動きを別々の枝で予測し融合する二重枝ニューラルネットを提案し、長期予測での動的物体の消失やぼやけを改善した。
著者: Maneekwan Toyungyernsub, Masha Itkina, Ransalu Senanayake, Mykel J. Kochenderfer
分類: cs.RO
原文アブストラクト
Predicting the future occupancy state of an environment is important to enable informed decisions for autonomous vehicles. Common challenges in occupancy prediction include vanishing dynamic objects and blurred predictions, especially for long prediction horizons. In this work, we propose a double-prong neural network architecture to predict the spatiotemporal evolution of the occupancy state. One prong is dedicated to predicting how the static environment will be observed by the moving ego vehicle. The other prong predicts how the dynamic objects in the environment will move. Experiments conducted on the real-world Waymo Open Dataset indicate that the fused output of the two prongs is capable of retaining dynamic objects and reducing blurriness in the predictions for longer time horizons than baseline models.
