Deep Tracking on the Move: Learning to Track the World from a Moving
Vehicle using Recurrent Neural Networks

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

29 September 2016

Dushyant Rao

Dominic Zeng Wang

Papers citing "Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks"

6 / 6 papers shown

Title
Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems Sahil Tomar Shamshe Alam Sandeep Kumar Amit Mathur 41 0 0 29 Apr 2025
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning K. S. Mann Abhishek Tomy Anshul K. Paigwar A. Renzaglia Christian Laugier 28 10 0 06 May 2022
Attention Augmented ConvLSTM for Environment Prediction Bernard Lange Masha Itkina Mykel J. Kochenderfer 71 21 0 19 Oct 2020
FISHING Net: Future Inference of Semantic Heatmaps In Grids Noureldin Hendy Cooper Sloan Fengshi Tian P. Duan Nick Charchut Yuxing Xie Chuan Wang James Philbin 23 76 0 17 Jun 2020
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids Lukas Hoyer Patrick Kesper Anna Khoreva Volker Fischer MDE 16 12 0 21 Mar 2019
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 227 7,903 0 13 Jun 2015