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This paper presents the FPGA design of a convolutional neural network (CNN) based road segmentation algorithm for real-time processing of LiDAR data.
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W. Wang and X. Huang, “An fpga co-processor for adaptive lane departure warning system,” in Circuits and Systems (ISCAS), 2013 IEEE International Symposium on . IEEE, 2013, pp. 1380–1383
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J. Fritsch, T. Kuehnl, and A. Geiger, “A new performance measure and evaluation benchmark for road detection algorithms,” in International Conference on Intelligent Transportation Systems (ITSC) , 2013
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Z. Chen, X. Huang, Z. Ni, and H. He, “A gpu-based real-time traffic sign detection and recognition system,” in Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 2014 IEEE Symposium on . IEEE, 2014, pp. 1–5
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J. Zhao, X. Huang, and Y. Massoud, “An efficient real-time fpga implementation for object detection,” in New Circuits and Systems Conference (NEWCAS), 2014 IEEE 12th International . IEEE, 2014, pp. 313–316
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J. Zhao, B. Xie, and X. Huang, “Real-time lane departure and front collision warning system on an fpga,” in High Performance Extreme Computing Conference (HPEC), 2014 IEEE . IEEE, 2014, pp. 1–5
2014
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M. Beyeler, F. Mirus, and A. Verl, “Vision-based robust road lane detection in urban environments,” in Robotics and Automation (ICRA), 2014 IEEE International Conference on . IEEE, 2014, pp. 4920–4925
2014
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G. B. Vitor, A. C. Victorino, and J. V. Ferreira, “A probabilistic distribution approach for the classification of urban roads in complex environments,” in IEEE Workshop on International Conference on Robotics and Automation , 2014
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2016
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Z. Chen and X. Huang, “Accurate and reliable detection of traffic lights using multiclass learning and multiobject tracking,” IEEE Intelligent Transportation Systems Magazine , vol. 8, no. 4, pp. 28–42, 2016
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A. B. Hillel, R. Lerner, D. Levi, and G. Raz, “Recent progress in road and lane detection: a survey,” Machine vision and applications , vol. 25, no. 3, pp. 727–745, 2014
2014
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R. Okuda, Y. Kajiwara, and K. Terashima, “A survey of technical trend of adas and autonomous driving,” in VLSI Technology, Systems and Application (VLSI-TSA), Proceedings of Technical Program-2014 International Symposium on . IEEE, 2014, pp. 1–4
2014
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P. Y. Shinzato, D. F. Wolf, and C. Stiller, “Road terrain detection: Avoiding common obstacle detection assumptions using sensor fusion,” in Intelligent Vehicles Symposium Proceedings, 2014 IEEE . IEEE, 2014, pp. 687–692
2014
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
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A. González, G. Villalonga, J. Xu, D. Vázquez, J. Amores, and A. M. López, “Multiview random forest of local experts combining rgb and lidar data for pedestrian detection,” in Intelligent Vehicles Symposium (IV), 2015 IEEE . IEEE, 2015, pp. 356–361
2015
Cited alongside, same era.
T. Chen, Z. Chen, Q. Shi, and X. Huang, “Road marking detection and classification using machine learning algorithms,” in Intelligent Vehicles Symposium (IV), 2015 IEEE . IEEE, 2015, pp. 617–621
2015
Cited alongside, same era.
L. Xiao, B. Dai, D. Liu, T. Hu, and T. Wu, “Crf based road detection with multi-sensor fusion,” in Intelligent Vehicles Symposium (IV), 2015 IEEE . IEEE, 2015, pp. 192–198
2015
Cited alongside, same era.
2015
Cited alongside, same era.
C. C. T. Mendes, V. Frémont, and D. F. Wolf, “Exploiting fully convolutional neural networks for fast road detection,” in Robotics and Automation (ICRA), 2016 IEEE International Conference on . IEEE, 2016, pp. 3174–3179
2016
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Y. Xiang, W. Choi, Y. Lin, and S. Savarese, “Subcategory-aware convolutional neural networks for object proposals and detection,” in Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on . IEEE, 2017, pp. 924–933
2017
Closest in time.
Z. Chen and X. Huang, “End-to-end learning for lane keeping of self-driving cars,” in Intelligent Vehicles Symposium (IV), 2017 IEEE . IEEE, 2017, pp. 1856–1860
2017
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L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using convolutional neural networks,” IEEE Intelligent Vehicles Symposium 2017 , 2017
2017
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G. L. Oliveira, C. Bollen, W. Burgard, and T. Brox, “Efficient and robust deep networks for semantic segmentation,” The International Journal of Robotics Research , p. 0278364917710542, 2017
2017
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L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu, and T. Wu, “Hybrid conditional random field based camera-lidar fusion for road detection,” Information Sciences , 2017
2017
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E. Romera, J. M. Alvarez, L. M. Bergasa, and R. Arroyo, “Efficient convnet for real-time semantic segmentation,” in Intelligent Vehicles Symposium (IV), 2017 IEEE . IEEE, 2017, pp. 1789–1794
2017
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L. Chen, J. Yang, and H. Kong, “Lidar-histogram for fast road and obstacle detection,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 1343–1348
2017
Closest in time.