IEEE Circuits and Systems Magazine - Q2 2021 - 71

[15] K. Guo, S. Zeng, J. Yu, Y. Wang, and H. Yang, " [DL] a survey of FPGAbased
neural network inference accelerators, " ACM Trans. Reconfigurable
Technol. Syst. (TRETS), vol. 12, no. 1, pp. 1-26, 2019. doi: 10.1145/
3289185.
[16] B. Yu, W. Hu, L. Xu, J. Tang, S. Liu, and Y. Zhu, " Building the computing
system for autonomous micromobility vehicles: Design constraints
and architectural optimizations, " in Proc. 53rd Annu. IEEE/ACM Int.
Symp. Microarch. (MICRO), 2020. doi: 10.1109/MICRO50266.2020.00089.
[17] N. Dalal and B. Triggs, " Histograms of oriented gradients for human
detection, " in Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern
Recogn. (CVPR'05), 2005, vol. 1, pp. 886-893.
[18] X He, R. S. Zemel, and M. A. Carreira-Perpinan, " Multiscale conditional
random fields for image labeling, " in Proc. IEEE Comput. Soc.
Conf. Comput. Vision and Pattern Recogn. (CVPR 2004), 2004, vol. 2, p. II.
[19] X. He, R. S. Zemel, and D. Ray, " Learning and incorporating topdown
cues in image segmentation, " in Proc. Comput. Vision - ECCV 2006,
A. Leonardis, H. Bischof, and A. Pinz, Eds. Berlin, Heidelberg: Springer
Berlin Heidelberg, 2006, pp. 338-351.
[20] Y. Xiang, A. Alahi, and S. Savarese, " Learning to track: Online multiobject
tracking by decision making, " in Proc. IEEE Int. Conf. Comput. Vision
(ICCV), 2015, pp. 4705-4713. doi: 10.1109/ICCV.2015.534.
[21] R. Girshick, " Fast R-CNN, " in Proc. IEEE Int. Conf. Comput. Vision
(ICCV), Dec. 2015. doi: 10.1109/ICCV.2015.169.
[22] S. Ren, K. He, R. B. Girshick, and J. Sun, " Faster R-CNN: Towards
real-time object detection with region proposal networks, " CoRR, vol.
abs/1506.01497, 2015.
[23] W. Liu et al., " SSD: Single shot multibox detector, " CoRR, vol. abs/
1512.02325, 2015.
[24] J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi, " You only
look once: Unified, real-time object detection, " CoRR, vol. abs/1506.
02640, 2015.
[25] J. Redmon and A. Farhadi, " YOLO9000: Better, faster, stronger, "
CoRR, vol. abs/1612.08242, 2016.
[26] J. Long, E. Shelhamer, and T. Darrell, " Fully convolutional networks
for semantic segmentation, " CoRR, vol. abs/1411.4038, 2014.
[27] K. He, X. Zhang, S. Ren, and J. Sun, " Spatial pyramid pooling in deep
convolutional networks for visual recognition, " CoRR, vol. abs/1406.
4729, 2014.
[28] H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, " Pyramid scene parsing
network, " CoRR, vol. abs/1612.01105, 2016.
[29] L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S.
Torr, " Fully-convolutional Siamese networks for object tracking, " CoRR,
vol. abs/1606.09549, 2016.
[30] H. Durrant-Whyte and T. Bailey, " Simultaneous localization and
mapping: Part I, " IEEE Robot. Automat. Mag., vol. 13, no. 2, pp. 99-110,
2006. doi: 10.1109/MRA.2006.1638022.
[31] M. Montemerlo et al., " Junior: The Stanford entry in the urban
challenge, " J. Field Robot., vol. 25, no. 9, pp. 569-597, 2008. doi: 10.1002/
rob.20258.
[32] J. Ziegler et al., " Making bertha drive-an autonomous journey on
a historic route, " IEEE Intell. Transp. Syst. Mag., vol. 6, no. 2, pp. 8-20,
2014. doi: 10.1109/MITS.2014.2306552.
[33] C. Katrakazas, M. Quddus, W.-H. Chen, and L. Deka, " Real-time motion
planning methods for autonomous on-road driving: State-of-theart
and future research directions, " Transp. Res. C, Emerg. Technol., vol.
60, pp. 416-442, 2015. doi: 10.1016/j.trc.2015.09.011.
[34] B. Paden, M. Cˇáp, S. Z. Yong, D. Yershov, and E. Frazzoli, " A survey
of motion planning and control techniques for self-driving urban vehicles, "
IEEE Trans. Intell. Veh., vol. 1, no. 1, pp. 33-55, 2016. doi: 10.1109/
TIV.2016.2578706.
[35] Y. Deng, Y. Chen, Y. Zhang, and S. Mahadevan, " Fuzzy dijkstra
algorithm for shortest path problem under uncertain environment, "
Appl. Soft Comput., vol. 12, no. 3, pp. 1231-1237, 2012. doi: 10.1016/j.asoc.
2011.11.011.
[36] P. E. Hart, N. J. Nilsson, and B. Raphael, " A formal basis for the
heuristic determination of minimum cost paths, " IEEE Trans. Syst. Sci.
Cybern., vol. 4, no. 2, pp. 100-107, 1968. doi: 10.1109/TSSC.1968.300136.
[37] S. M. LaValle and J. J. Kuffner Jr., " Randomized kinodynamic planning, "
Int. J. Robot. Res., vol. 20, no. 5, pp. 378-400, 2001. doi: 10.1177/
02783640122067453.
[38] L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, " Probabilistic
roadmaps for path planning in high-dimensional configuration
spaces, " IEEE Trans. Robot. Autom. (1989-June 2004), vol. 12, no. 4, pp.
566-580, 1996. doi: 10.1109/70.508439.
SECOND QUARTER 2021
[39] S. Shalev-Shwartz, N. Ben-Zrihem, A. Cohen, and A. Shashua, " Longterm
planning by short-term prediction, " 2016, arXiv:1602.01580.
[40] M. Gómez, R. González, T. Martínez-Marín, D. Meziat, and S. Sánchez,
" Optimal motion planning by reinforcement learning in autonomous
mobile vehicles, " Robotica, vol. 30, no. 2, pp. 159, 2012. doi: 10.1017/
S0263574711000452.
[41] S. Shalev-Shwartz, S. Shammah, and A. Shashua, " Safe, multi-agent,
reinforcement learning for autonomous driving, " 2016, arXiv:1610.03295.
[42] M. Bojarski et al., " End to end learning for self-driving cars, " 2016,
arXiv:1604.07316.
[43] X. Geng, H. Liang, B. Yu, P. Zhao, L. He, and R. Huang, " A scenarioadaptive
driving behavior prediction approach to urban autonomous
driving, " Appl. Sci., vol. 7, no. 4, p. 426, 2017. doi: 10.3390/app7040426.
[44] C. J. Watkins and P. Dayan, " Q-learning, " Mach. Learn., vol. 8, no.
3-4, pp. 279-292, 1992. doi: 10.1023/A:1022676722315.
[45] V. R. Konda and J. N. Tsitsiklis, " Actor-critic algorithms, " in Advances
Neural Inf. Process. Syst., 2000, pp. 1008-1014.
[46] S. L. Hicks, I. Wilson, L. Muhammed, J. Worsfold, S. M. Downes, and
C. Kennard, " A depth-based head-mounted visual display to aid navigation
in partially sighted individuals, " PloS One, vol. 8, no. 7, p. e67695,
2013. doi: 10.1371/journal.pone.0067695.
[47] T. Whelan, R. F. Salas-Moreno, B. Glocker, A. J. Davison, and S. Leutenegger,
" Elasticfusion: Real-time dense slam and light source estimation, "
Int. J. Robot. Res., vol. 35, no. 14, pp. 1697-1716, 2016. doi: 10.1177/
0278364916669237.
[48] V. A. Prisacariu et al., " Infinitam v3: A framework for large-scale 3d
reconstruction with loop closure, " 2017, arXiv:1708.00783.
[49] S. Golodetz, T. Cavallari, N. A. Lord, V. A. Prisacariu, D. W. Murray,
and P. H. Torr, " Collaborative large-scale dense 3d reconstruction with
online inter-agent pose optimisation, " IEEE Trans. Vis. Comput. Graphics,
vol. 24, no. 11, pp. 2895-2905, 2018. doi: 10.1109/TVCG.2018.2868533.
[50] M. Pérez-Patricio and A. Aguilar-González, " FPGA implementation
of an efficient similarity-based adaptive window algorithm for real-time
stereo matching, " J. Real-Time Image Process., vol. 16, no. 2, pp. 271-287,
2019. doi: 10.1007/s11554-015-0530-6.
[51] D.-W. Yang, L.-C. Chu, C.-W. Chen, J. Wang, and M.-D. Shieh, " Depthreliability-based
stereo-matching algorithm and its VLSI architecture
design, " IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 6, pp. 1038-
1050, 2014. doi: 10.1109/TCSVT.2014.2361419.
[52] A. Aguilar-González and M. Arias-Estrada, " An FPGA stereo matching
processor based on the sum of hamming distances, " in Proc. Int.
Symp. Appl. Reconfigurable Comput., 2016, pp. 66-77.
[53] M. Pérez-Patricio, A. Aguilar-González, M. Arias-Estrada, H.-R. Hernandez-de
Leon, J.-L. Camas-Anzueto, and J. de Jesús Osuna-Coutiño,
" An FPGA stereo matching unit based on fuzzy logic, " Microprocessors
Microsyst., vol. 42, pp. 87-99, 2016. doi: 10.1016/j.micpro.2015.10.011.
[54] G. Cocorullo, P. Corsonello, F. Frustaci, and S. Perri, " An efficient
hardware-oriented stereo matching algorithm, " Microprocessors Microsyst.,
vol. 46, pp. 21-33, 2016. doi: 10.1016/j.micpro.2016.09.010.
[55] P. M. Santos, J. C. Ferreira, and J. S. Matos, " Scalable hardware architecture
for disparity map computation and object location in realtime, "
J. Real-Time Image Process., vol. 11, no. 3, pp. 473-485, 2016. doi:
10.1007/s11554-013-0338-1.
[56] K. M. Ali, R. B. Atitallah, N. Fakhfakh, and J.-L. Dekeyser, " Exploring
HLS optimizations for efficient stereo matching hardware implementation, "
in Proc. Int. Symp. Appl. Reconfigurable Comput., 2017, pp. 168-176.
[57] B. McCullagh, " Real-time disparity map computation using the cell
broadband engine, " J. Real-Time Image Process., vol. 7, no. 2, pp. 87-93,
2012. doi: 10.1007/s11554-010-0155-8.
[58] L. Li, X. Yu, S. Zhang, X. Zhao, and L. Zhang, " 3d cost aggregation
with multiple minimum spanning trees for stereo matching, " Appl. Opt.,
vol. 56, no. 12, pp. 3411-3420, 2017. doi: 10.1364/AO.56.003411.
[59] D. Zha, X. Jin, and T. Xiang, " A real-time global stereo-matching
on FPGA, " Microprocessors Microsyst., vol. 47, pp. 419-428, 2016. doi:
10.1016/j.micpro.2016.08.005.
[60] L. Puglia, M. Vigliar, and G. Raiconi, " Real-time low-power FPGA architecture
for stereo vision, " IEEE Trans. Circuits Syst. II, Express Briefs,
vol. 64, no. 11, pp. 1307-1311, 2017. doi: 10.1109/TCSII.2017.2691675.
[61] A. Kjær-Nielsen et al., " A two-level real-time vision machine combining
coarse-and fine-grained parallelism, " J. Real-Time Image Process.,
vol. 5, no. 4, pp. 291-304, 2010. doi: 10.1007/s11554-010-0159-4.
[62] S. Wong, S. Vassiliadis, and S. Cotofana, " A sum of absolute differences
implementation in FPGA hardware, " in Proc. 28th Euromicro Conf.,
2002, pp. 183-188.
IEEE CIRCUITS AND SYSTEMS MAGAZINE
71
IEEE Circuits and Systems Magazine - Q2 2021

Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q2 2021
