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The primary focus of autonomous driving research is to improve driving accuracy.
The Cost of Accidents: A Legal and Economic Analysis
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Advances in neural information processing systems 1
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S. Hochreiter and J. Schmidhuber · 1997
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Y. LeCun, U. Muller, J. Ben, E. Cosatto, and B. Flepp · 2005
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A statistical confidence measure for optical flows
C. Kondermann, R. Mester, and C. Garbe · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Object-oriented bayesian networks for detection of lane change maneuvers
D. Kasper, G. Weidl, T. Dang, G. Breuel, A. Tamke, A. Wedel, and W. Rosenstiel · 2012
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Quality prediction for image completion
J. Kopf, W. Kienzle, S. Drucker, and S. B. Kang · 2012
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Learning a confidence measure for optical flow
O. M. Aodha, A. Humayun, M. Pollefeys, and G. J. Brostow · 2013
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Multi-target tracking using a 3d-lidar sensor for autonomous vehicles
J. Choi, S. Ulbrich, B. Lichte, and M. Maurer · 2013
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Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Autonomous vehicle technology: A guide for policymakers
J. M. Anderson, K. Nidhi, K. D. Stanley, P. Sorensen, C. Samaras, and O. A. Oluwatola · 2014
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A multi-sensor fusion system for moving object detection and tracking in urban driving environments
H. Cho, Y. W. Seo, B. V. K. V. Kumar, and R. R. Rajkumar · 2014
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The synthesizability of texture examples
D. Dai, H. Riemenschneider, and L. Van Gool · 2014
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3d traffic scene understanding from movable platforms
A. Geiger, M. Lauer, C. Wojek, C. Stiller, and R. Urtasun · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Look at the driver, look at the road: No distraction! no accident!
M. Rezaei and R. Klette · 2014
Cited alongside, same era.
Semiautonomous vehicular control using driver modeling
V. A. Shia, Y. Gao, R. Vasudevan, K. D. Campbell, T. Lin, F. Borrelli, and R. Bajcsy · 2014
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Dynamic probabilistic drivability maps for lane change and merge driver assistance
S. Sivaraman and M. M. Trivedi · 2014
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Predicting failures of vision systems
P. Zhang, J. Wang, A. Farhadi, M. Hebert, and D. Parikh · 2014
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Automated driving: The role of forecasts and uncertainty—a control perspective
A. Carvalho, S. Lefévre, G. Schildbach, J. Kong, and F. Borrelli · 2015
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Deepdriving: Learning affordance for direct perception in autonomous driving
C. Chen, A. Seff, A. Kornhauser, and J. Xiao · 2015
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Intent-aware long-term prediction of pedestrian motion
V. Karasev, A. Ayvaci, B. Heisele, and S. Soatto · 2016
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Looking at humans in the age of self-driving and highly automated vehicles
E. Ohn-Bar and M. M. Trivedi · 2016
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Simultaneous perception and path generation using fully convolutional neural networks
L. Caltagirone, M. Bellone, L. Svensson, and M. Wahde · 2017
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Brain inspired cognitive model with attention for self-driving cars
S. Chen, S. Zhang, J. Shang, B. Chen, and N. Zheng · 2017
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Cited alongside, same era.
Car that knows before you do: Anticipating maneuvers via learning temporal driving models
A. Jain, H. S. Koppula, B. Raghavan, S. Soh, and A. Saxena · 2015
Cited alongside, same era.
Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding
A. Kendall, V. Badrinarayanan, and R. Cipolla · 2015
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Leveraging stereo matching with learning-based confidence measures
M.-G. Park and K.-J. Yoon · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Driver gaze tracking and eyes off the road detection system
F. Vicente, Z. Huang, X. Xiong, F. De la Torre, W. Zhang, and D. Levi · 2015
Cited alongside, same era.
Multi-view 3d object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
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Predicting scene parsing and motion dynamics in the future
X. Jin, H. Xiao, X. Shen, J. Yang, Z. Lin, Y. Chen, Z. Jie, J. Feng, and S. Yan · 2017
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Interpretable learning for self-driving cars by visualizing causal attention
J. Kim and J. Canny · 2017
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Predicting deeper into the future of semantic segmentation
P. Luc, N. Neverova, C. Couprie, J. Verbeek, and Y. LeCun · 2017
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1 year, 1000 km: The oxford robotcar dataset
W. Maddern, G. Pascoe, C. Linegar, and P. Newman · 2017
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End-to-end learning of driving models from large-scale video datasets
H. Xu, Y. Gao, F. Yu, and T. Darrell · 2017
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Domain adaptive faster r-cnn for object detection in the wild
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool · 2018
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Learning Driving Models with a Surround-View Camera System and a Route Planner
S. Hecker, D. Dai, and L. Van Gool · 2018
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Semantic Foggy Scene Understanding with Synthetic Data
C. Sakaridis, D. Dai, and L. Van Gool · 2018
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Anticipating traffic accidents with adaptive loss and large-scale incident db
T. Suzuki, H. Kataoka, Y. Aoki, and Y. Satoh · 2018
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