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How Uber's Self-Driving Technology Could Have Failed In The Fatal Tempe Crash | [pdf] | Forbes, 2018 |
Jim McPherson | ||
Debugging Machine Learning Tasks | [pdf] | arXiv preprint , 2018 |
Aleksandar Chakarov, Aditya Nori, Sriram Rajamani, Shayak Sen, Deepak Vijaykeerthy | ||
On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems | [pdf] | AAAI, 2017 |
BBesmira Nushi, Ece Kamar, Eric Horvitz, Donald Kossmann | ||
A Fault-Value Injection Approach for Multiple-Weight-Fault Tolerance of MNNs | [pdf] | Proceedings of the IEEE-INNS-ENNS, 2000 |
Itsuo Takanami, Masaru Sato, Yun Ping Yang | ||
An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software | [pdf] | CoRR, 2017 |
Rick Salay, Rodrigo Queiroz, Krzysztof Czarnecki | ||
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks | [pdf] | CAV, 2017 |
Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer |
NuScenes data collection | [pdf] | 2018 |
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Computer vision for autonomous vehicles: Problems, datasets and state-of-the-art | [pdf] | 2014 |
Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger | ||
When to use what data set for your self-driving car algorithm: An overview of publicly available driving datasets | [pdf] | IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017 |
Hang Yin, Christian Berger | ||
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling | [pdf] | CoRR, 2018 |
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, Trevor Darrell | ||
Berkeley DeepDrive data collection | [pdf] | 2018 |
A Multi-sensor Traffic Scene Dataset with Omnidirectional Video | [pdf] | Ground Truth - What is a good dataset? CVPR Workshop, 2013 |
P. Koschorrek, T. Piccini, P. berg, M. Felsberg, L. Nielsen, R. Mester | ||
MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation | [pdf] | CoRR, 2017 |
Lex Fridman, Daniel E. Brown, Michael Glazer, William Angell, Spencer Dodd, Benedikt Jenik, Jack Terwilliger, Julia Kindelsberger, Li Ding, Sean Seaman, Hillary Abraham, Alea Mehler, Andrew Sipperley, Anthony Pettinato, Linda Angell, Bruce Mehler, Bryan Reimer | ||
Ford campus vision and lidar data set | [pdf] | International Journal of Robotics Research, 2011 |
Gaurav Pandey, James R. McBride, Ryan Michael Eustice | ||
Vision meets robotics: the KITTI dataset | [pdf] | The International Journal of Robotics Research, 2013 |
A. Geiger, P. Lenz, C. Stiller, R. Urtasun | ||
Udacity data collection | [pdf] | 2018 |
Cityscapes data collection | [pdf] | 2018 |
The Oxford data collection | [pdf] | 2018 |
Semantic object classes in video: A high-definition ground truth database | [pdf] | Pattern Recognition Letters, 2009 |
Gabriel J. Brostow, Julien Fauqueur, Roberto Cipolla | ||
The cambridge-driving labeled video database | [pdf] | 2018 |
Daimler pedestrian database | [pdf] | 2018 |
Caltech pedestrian database | [pdf] | 2018 |
Pyramid scene parsing network | [pdf] | IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 |
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia | ||
Sgn: Sequential grouping networks for instance segmentation | [pdf] | 2017 |
Shu Liu, Jiaya Jia, Sanja Fidler, Raquel Urtasun | ||
Segmentation and Recognition using Structure from Motion Point Clouds | [pdf] | ECCV, 2008 |
Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, Roberto Cipolla | ||
Daimler Pedestrian Segmentation Benchmark Dataset | [pdf] | Proc. of the British Machine Vision Conference, 2013 |
F. Flohr, D. M. Gavrila. | ||
A new benchmark for vision-based cyclist detection | [pdf] | IEEE Intelligent Vehicles Symposium (IV), 2016 |
X. Li, F. Flohr, Y. Yang, H. Xiong, M. Braun, S. Pan, K. Li, D. M. Gavrila | ||
Pedestrian detection: A benchmark | [pdf] | IEEE Conference on Computer Vision and Pattern Recognition, 2009 |
P. Dollar, C. Wojek, B. Schiele, and P. Perona | ||
Velodyne LiDAR for data collection | [pdf] | 2018 |
Sick LiDAR for data collection | [pdf] | 2018 |
NVIDIA AI car computer Drive PX | [pdf] | |
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Tegra X2 | [pdf] | |
DenverCore | [pdf] | |
Pascal microarchitecture | [pdf] | |
NVIDIA Drive AGX | [pdf] | |
NVIDIA Volta | [pdf] | |
R-CarV3H | [pdf] | |
R-CarH3 | [pdf] | |
Can fpgas beat gpus in accelerating next-generation deep neural networks? | [pdf] | Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017 |
E. Nurvitadhi, G. Venkatesh, J. Sim, D. Marr, R. Huang, J. Ong Gee Hock, Y. T. Liew, K. Srivatsan, D. Moss, S. Subhaschandra, G. Boudoukh | ||
Accelerating Deep Convolutional Neural Networks Using Specialized Hardware | [pdf] | 2015 |
Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, Jeremy Fowers, Karin Strauss, Eric S. Chung | ||
Understanding performance differences of fpgas and gpus: (abtract only) | [pdf] | Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2018 |
Jason Cong, Zhenman Fang, Michael Lo, Hanrui Wang, Jingxian Xu, Shaochong Zhang |