Page 40 - First special issue on The impact of Artificial Intelligence on communication networks and services
P. 40
,78 -2851$/ ,&7 'LVFRYHULHV 9RO 0DUFK
[7] Directive 2010/40/EU of the European Parliament and [20] V. Gaikwad and S. Lokhande, “Lane departure identi-
of the council, “Directives on the framework for the de- fication for advanced driver assistance,” IEEE Trans-
ployment of intelligent transport systems in the field of actions on Intelligent Transportation Systems, vol. 16,
road transport and for interfaces with other modes of no. 2, pp. 910–918, 2015.
transport,” Tech. Rep., 2010.
[21] A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi,
[8] European Comission. Directorate-General for Mobility “Shape-based pedestrian detection,” in Intelligent Ve-
and Transport, White Paper on Transport: Roadmap to hicles Symposium, 2000. IV 2000. Proceedings of the
a Single European Transport Area: Towards a Compet- IEEE. IEEE, 2000, pp. 215–220.
itive and Resource-efficient Transport System. Publi-
[22] J. R. Uijlings, K. E. Van De Sande, T. Gevers, and
cations Office of the European Union, 2011.
A. W. Smeulders, “Selective search for object recog-
[9] European Comission, “Preliminary descriptions of re- nition,” International journal of computer vision, vol.
search and innovation areas and fields, research and 104, no. 2, pp. 154–171, 2013.
innovation for europe’s future mobility,” Tech. Rep.,
[23] D. G. Lowe, “Distinctive image features from scale-
2012.
invariant keypoints,” International journal of computer
[10] J. Janai, F. G¨ uney, A. Behl, and A. Geiger, “Computer vision, vol. 60, no. 2, pp. 91–110, 2004.
vision for autonomous vehicles: Problems, datasets
[24] N. Dalal and B. Triggs, “Histograms of oriented gradi-
and state-of-the-art,” arXiv preprint arXiv:1704.05519,
ents for human detection,” in Computer Vision and Pat-
2017.
tern Recognition, 2005. CVPR 2005. IEEE Computer
Society Conference on, vol. 1. IEEE, 2005, pp. 886–
[11] Intel, “Intel atom processor e3900 series.”
893.
[Online]. Available: https://www.qualcomm.com/
solutions/automotive/drive-data-platform
[25] P. Viola and M. Jones, “Rapid object detection using a
boosted cascade of simple features,” in Computer Vi-
[12] Qualcomm, “Drive data platform.” [Online]. sion and Pattern Recognition, 2001. CVPR 2001. Pro-
Available: https://www.qualcomm.com/solutions/ ceedings of the 2001 IEEE Computer Society Confer-
automotive/drive-data-platform
ence on, vol. 1. IEEE, 2001, pp. I–I.
[13] N. Corp., “NVIDIA drive PX - the AI car computer
[26] Y. Freund and R. E. Schapire, “A desicion-theoretic
for autonomous driving,” 2017. [Online]. Available:
generalization of on-line learning and an application
http://www.nvidia.com/object/drive-px.html
to boosting,” in European conference on computational
learning theory. Springer, 1995, pp. 23–37.
[14] NVIDIA CUDA, “NVIDIA CUDA C programming
guide,” Nvidia Corporation, vol. 120, no. 18, p. 8, [27] C. Cortes and V. Vapnik, “Support vector machine,”
2011. Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
[15] S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, [28] P. Felzenszwalb, D. McAllester, and D. Ramanan, “A
J. Tran, B. Catanzaro, and E. Shelhamer, “cuDNN: discriminatively trained, multiscale, deformable part
Efficient primitives for deep learning,” arXiv preprint model,” in Computer Vision and Pattern Recognition,
arXiv:1410.0759, 2014. 2008. CVPR 2008. IEEE Conference on. IEEE, 2008,
pp. 1–8.
[16] Xilinx, “Xilinx automotive Zynq-7000.” [Online].
Available: https://www.xilinx.com/publications/prod\ [29] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Ima-
mktg/ZynqAuto\ ProdBrf.pdf genet classification with deep convolutional neural net-
works,” in Advances in neural information processing
[17] Altera, “A safety methodology for ADAS systems, 2012, pp. 1097–1105.
designs in FPGAs.” [Online]. Avail-
able: https://www.altera.com/en\ US/pdfs/literature/ [30] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and
wp/wp-01204-automotive-functional-safety.pdf L. Fei-Fei, “Imagenet: A large-scale hierarchical image
database,” in Computer Vision and Pattern Recognition,
[18] Mobileye, “The evolution of EyeQ,” 2017. [Online]. 2009. CVPR 2009. IEEE Conference on. IEEE, 2009,
Available: https://www.mobileye.com/our-technology/ pp. 248–255.
evolution-eyeq-chip/
[31] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed,
[19] J. Son, H. Yoo, S. Kim, and K. Sohn, “Real-time illumi- D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabi-
nation invariant lane detection for lane departure warn- novich, “Going deeper with convolutions,” in Proceed-
ing system,” Expert Systems with Applications, vol. 42, ings of the IEEE conference on computer vision and
no. 4, pp. 1816–1824, 2015. pattern recognition, 2015, pp. 1–9.
,QWHUQDWLRQDO 7HOHFRPPXQLFDWLRQ 8QLRQ