The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Deep Reinforcement Learning framework for Autonomous Driving
Open accessAbstract
Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
(1999) · Cited by 5,147
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198
Research works printed but not linked: 5
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature , 521 (7553), 436-444.
- Mnih, V., Heess, N., Graves, A., & others. (2014). Recurrent mod- els of visual attention. Advances in Neural Information Processing Systems, (pp. 2204-2212).
- Ondruska, P., & Posner, I. (2016). Deep tracking: Seeing beyond seeing using recurrent neural networks. arXiv preprint arXiv:1602.00991. · arXiv
- Watkins, C. J. (1989). Learning from delayed rewards. Ph.D. disser- tation, University of Cambridge England.
- Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine learning , 8 (3-4), 279-292.
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Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Self-citationA Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“introduced in [60], where the network was tested in a simulation environment.”
BackgroundA survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Park, S.-O., Jeong, H., Park, J., Bae, J., & Choi, S. (2022) · Nature Communications · doi:10.1038/s41467-022-30539-6 · Cited by 331
“Moreover, the artificial neuron’s responses with different combinations of the circuit components are measured; (1) different capacitance values and (2) different capacitance and resistance values while keeping the same R × C value (see Supplementary Figs.”
Background
Metadata
- Authors
- Sallab, A. E., Abdou, M., Perot, E., & Yogamani, S.
- Affiliations
- Valeo (France) (FR)
- Valeo (Ireland) (IE)
- ORCID
- Abdou, Mohammed
- Venue
- Electronic Imaging
- Published
- 2017-01-29
- Volume
- 29
- Issue
- 19
- Pages
- 70-76
- ISSN
- 2470-1173 (ISSN-L)
- OpenAlex
- W2583993537
- Cited by
- 1,045
- References
- 17
- Citations by year
- 2026: 28 · 2025: 93 · 2024: 105 · 2023: 120 · 2022: 210 · 2021: 203 · 2020: 160 · 2019: 79 · 2018: 41 · 2017: 6
- FWCI
- 46.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Electronic Imaging (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Institut national de recherche en informatique et en automatique (INRIA)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- autonomous driving, deep reinforcement learning, recurrent neural networks, attention-based models, partially observable environments, TORCS, vehicle maneuvering
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