END TO END LEARNING FOR A DRIVING SIMULATOR
Abstract
Convolutional network approach is utilized for training an end-to-end model that would let a car drive itself around the track in a driving simulator by predicting steering angles based on the simulated camera data.
About the Authors
V. F. Alexeev
Belarusian state university of informatics and radioelectronics
Belarus
Belarus
A. I. Staravoitau
Belarusian state university of informatics and radioelectronics
Belarus
Belarus
G. A. Piskun
Belarusian state university of informatics and radioelectronics
Belarus
Belarus
D. V. Likhacheuski
Belarusian state university of informatics and radioelectronics
Belarus
Belarus
References
1. Krizhevsky A., Sutskever I., Hinton G.E. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. Curran Associates, Inc., 2012. P. 1097-1105.
2. End to end learning for self-driving cars / M. Bojarski [et al.]. 2016. arXiv preprint arXiv:1604.07316.
3. Net-Scale Technologies, Inc. Autonomous off-road vehicle control using end-to-end learning, July 2004. Final technical report [Electronic resource]. - URL: http://net-scale.com/doc/net-scale-dave-report.pdf (access date: 01.03.2018).
4. Udacity Inc., Udacity's Self-Driving Car Simulator, GitHub repository [Electronic resource]. - URL: https://github.com/udacity/self-driving-car-sim (access date: 01.03.2018).
5. Diederik P.K., Jimmy B.A. A Method for Stochastic Optimization. 2017. arXiv preprint arXiv:1412.6980v9.
6. Glorot X., Bordes A., Bengio Y. Deep sparse rectifier neural networks. AISTATS [Electronic resource]. - URL: http://jmlr.org/proceedings/papers/v15/glorot11a/glorot11a.pdf (access date: 01.03.2018).
7. Scherer D., Müller A.C., Behnke S. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition // 20th International Conference «Artificial Neural Networks (ICANN)». Thessaloniki, Greece, 2010. P. 92-101.
8. Subject independent facial expression recognition with robust face detection using a convolutional neural network / Matusugu M. [et al.]. Neural Networks. 2003. № 16 (5). P. 555-559.
Review
For citations:
Alexeev V.F., Staravoitau A.I., Piskun G.A., Likhacheuski D.V. END TO END LEARNING FOR A DRIVING SIMULATOR. Doklady BGUIR. 2018;(2):85-91. (In Russ.)
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