Fetching the paper…
Reading the bibliography…
We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles.
Occupancy grids: A stochastic spatial representation for active robot perception
A. Elfes · 1990
Earlier work this paper cites.
Two-frame motion estimation based on polynomial expansion
G. Farnebäck · 2003
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2003
Earlier work this paper cites.
A mixture-model based algorithm for real-time terrain estimation
I. Miller and M. Campbell · 2007
Earlier work this paper cites.
Optimal global path planning in time varying environments based on a cost evaluation function
O. K. Gupta and R. A. Jarvis · 2008
Earlier work this paper cites.
Model based vehicle detection and tracking for autonomous urban driving
A. Petrovskaya and S. Thrun · 2009
Earlier work this paper cites.
Object detection and tracking for autonomous navigation in dynamic environments
A. Ess, K. Schindler, B. Leibe, and L. Van Gool · 2010
Earlier work this paper cites.
Collision avoidance method for mobile robot considering motion and personal spaces of evacuees
T. Ohki, K. Nagatani, and K. Yoshida · 2010
Earlier work this paper cites.
An approach for 2d visual occupancy grid map using monocular vision
A. M. Santana, K. R. Aires, R. M. Veras, and A. A. Medeiros · 2011
Earlier work this paper cites.
Mobile robot path planning using human prediction model based on massive trajectories
H. Noguchi, T. Yamada, T. Mori, and T. Sato · 2012
Cited alongside, same era.
Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Cited alongside, same era.
Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
Cited alongside, same era.
Deep tracking: Seeing beyond seeing using recurrent neural networks
P. Ondruska and I. Posner · 2016
Later among the works it cites.
A review of global path planning methods for occupancy grid maps regardless of obstacle density
E. Tsardoulias, A. Iliakopoulou, A. Kargakos, and L. Petrou · 2016
Later among the works it cites.
S. Hoermann, M. Bach, and K. Dietmayer · 2017
Later among the works it cites.
State initialization for recurrent neural network modeling of time-series data
N. Mohajerin and S. L. Waslander · 2017
Later among the works it cites.
Deep tracking in the wild: End-to-end tracking using recurrent neural networks
J. Dequaire, P. Ondrúška, D. Rao, D. Wang, and I. Posner · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Srivastava, E. Mansimov, and R. Salakhudinov · 2015
Cited alongside, same era.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo · 2015
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
Cited alongside, same era.
Deep predictive coding networks for video prediction and unsupervised learning
W. Lotter, G. Kreiman, and D. Cox · 2016
Cited alongside, same era.
Deep object tracking on dynamic occupancy grid maps using rnns
N. Engel, S. Hoermann, P. Henzler, and K. Dietmayer · 2018
Closest in time.
Deep learning a quadrotor dynamic model for multi-step prediction
N. Mohajerin, M. Mozifian, and S. L. Waslander · 2018
Closest in time.
A random finite set approach for dynamic occupancy grid maps with real-time application
D. Nuss, S. Reuter, M. Thom, T. Yuan, G. Krehl, M. Maile, A. Gern, and K. Dietmayer · 2018
Closest in time.
End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
S. Wang, R. Clark, H. Wen, and N. Trigoni · 2018
Closest in time.