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Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction
  via Self-Supervision

Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision

6 June 2023
Faris Janjos
Max Keller
Maxim Dolgov
J. Marius Zöllner
ArXivPDFHTML

Papers citing "Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision"

8 / 8 papers shown
Title
Model-Based Imitation Learning for Urban Driving
Model-Based Imitation Learning for Urban Driving
Anthony Hu
Gianluca Corrado
Nicolas Griffiths
Zak Murez
Corina Gurau
Hudson Yeo
Alex Kendall
R. Cipolla
Jamie Shotton
104
135
0
14 Oct 2022
Learning Dynamics Models for Model Predictive Agents
Learning Dynamics Models for Model Predictive Agents
M. Lutter
Leonard Hasenclever
Arunkumar Byravan
Gabriel Dulac-Arnold
Piotr Trochim
N. Heess
J. Merel
Yuval Tassa
AI4CE
52
26
0
29 Sep 2021
Self-Supervised Action-Space Prediction for Automated Driving
Self-Supervised Action-Space Prediction for Automated Driving
Faris Janjos
Maxim Dolgov
J. Marius Zöllner
18
12
0
21 Sep 2021
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via
  Differentiable Simulation
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
Adam Scibior
Vasileios Lioutas
Daniele Reda
Peyman Bateni
Frank D. Wood
VGen
40
47
0
22 Apr 2021
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction
  and Tracking
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
Jiachen Li
Hengbo Ma
Zhihao Zhang
Jinning Li
M. Tomizuka
50
68
0
18 Feb 2021
What-If Motion Prediction for Autonomous Driving
What-If Motion Prediction for Autonomous Driving
Siddhesh Khandelwal
William Qi
Jagjeet Singh
Andrew Hartnett
Deva Ramanan
114
115
0
24 Aug 2020
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Sergio Casas
Cole Gulino
Simon Suo
Katie Z Luo
Renjie Liao
R. Urtasun
147
155
0
23 Jul 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
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