Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

The laplacian pyramid as a compact image code

P. Burt and E. Adelson · 1983

Earlier work this paper cites.

Alvinn, an autonomous land vehicle in a neural network

D. A. Pomerleau · 1989

Earlier work this paper cites.

A density-based algorithm for discovering clusters in large spatial databases with noise

M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al · 1996

Earlier work this paper cites.

Long short-term memory

S. Hochreiter and J. Schmidhuber · 1997

Earlier work this paper cites.

Forecasting with exponential smoothing: the state space approach

R. Hyndman, A. B. Koehler, J. K. Ord, and R. D. Snyder · 2008

Earlier work this paper cites.

Autonomous driving in urban environments: Boss and the urban challenge

C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer, et al · 2008

Earlier work this paper cites.

The DARPA urban challenge: autonomous vehicles in city traffic

M. Buehler, K. Iagnemma, and S. Singh · 2009

Earlier work this paper cites.

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2009

Earlier work this paper cites.

Understanding the difficulty of training deep feedforward neural networks

X. Glorot and Y. Bengio · 2010

Earlier work this paper cites.

Towards fully autonomous driving: Systems and algorithms

J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J. Z. Kolter, D. Langer, O. Pink, V. Pratt, et al · 2011

Earlier work this paper cites.

Vehicle dynamics and control

R. Rajamani · 2011

Cited alongside, same era.

Adam: A method for stochastic optimization

Original

D. Kingma and J. Ba · 2014

Cited alongside, same era.

Dropout: a simple way to prevent neural networks from overfitting

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014

Cited alongside, same era.

Visualizing and understanding convolutional networks

M. D. Zeiler and R. Fergus · 2014

Cited alongside, same era.

Tensorflow: Large-scale machine learning on heterogeneous systems, 2015

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2015

Cited alongside, same era.

Deepdriving: Learning affordance for direct perception in autonomous driving

C. Chen, A. Seff, A. Kornhauser, and J. Xiao · 2015

Visualbackprop: visualizing cnns for autonomous driving

Original

M. Bojarski, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, U. Muller, and K. Zieba · 2016

Later among the works it cites.

End to end learning for self-driving cars

Original

M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016

Later among the works it cites.

Generating visual explanations

L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell · 2016

Later among the works it cites.

Densecap: Fully convolutional localization networks for dense captioning

J. Johnson, A. Karpathy, and L. Fei-Fei · 2016

Later among the works it cites.

End-to-end learning of driving models from large-scale video datasets

Original

H. Xu, Y. Gao, F. Yu, and T. Darrell · 2016