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A. Hata and D. Wolf, “Road marking detection using lidar reflective intensity data and its application to vehicle localization,” in 17th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2014, pp. 584–589
2014
Cited alongside, same era.
2017
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C. Jang, C. Kim, K. Jo, and M. Sunwoo, “Design factor optimization of 3d flash lidar sensor based on geometrical model for automated vehicle and advanced driver assistance system applications,” International Journal of Automotive Technology , vol. 18, no. 1, pp. 147–156, 2017
2017
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Q. Ha, K. Watanabe, T. Karasawa, Y. Ushiku, and T. Harada, “Mfnet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes,” in Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 5108–5115
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2017
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2017
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2017
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M. Hejase, J. Jing, J. M. Maroli, Y. B. Salamah, L. Fiorentini, and Ü. Özgüner, “Constrained backward path tracking control using a plug-in jackknife prevention system for autonomous tractor-trailers,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 2012–2017
2017
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2017
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J. K. Suhr, J. Jang, D. Min, and H. G. Jung, “Sensor fusion-based low-cost vehicle localization system for complex urban environments,” IEEE Transactions on Intelligent Transportation Systems , vol. 18, no. 5, pp. 1078–1086, 2017
2017
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J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 6517–6525, 2017
2017
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2017
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V. Peretroukhin, W. Vega-Brown, N. Roy, and J. Kelly, “PROBE-GK: Predictive robust estimation using generalized kernels,” pre-print , Aug. 2017
2017
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2017
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Y. Zhou and O. Tuzel, “VoxelNet: End-to-End learning for point cloud based 3D object detection,” Nov. 2017
2017
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L. Liu, Z. Pan, and B. Lei, “Learning a rotation invariant detector with rotatable bounding box,” Nov. 2017
2017
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X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3D object detection network for autonomous driving,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017, pp. 6526–6534
2017
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C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum PointNets for 3D object detection from RGB-D data,” Nov. 2017
2017
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2017
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2017
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2017
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2017
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H. Darweesh, E. Takeuchi, K. Takeda, Y. Ninomiya, A. Sujiwo, L. Y. Morales, N. Akai, T. Tomizawa, and S. Kato, “Open source integrated planner for autonomous navigation in highly dynamic environments,” Journal of Robotics and Mechatronics , vol. 29, no. 4, pp. 668–684, 2017
2017
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2017
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2017
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2017
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2017
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2017
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2017
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2017
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2018
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2018
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2018
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2018
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2018
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Y. Tian, K. Pei, S. Jana, and B. Ray, “Deeptest: Automated testing of deep-neural-network-driven autonomous cars,” in Proceedings of the 40th International Conference on Software Engineering . ACM, 2018, pp. 303–314
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2018
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2018
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2018
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E. Yurtsever, S. Yamazaki, C. Miyajima, K. Takeda, M. Mori, K. Hitomi, and M. Egawa, “Integrating driving behavior and traffic context through signal symbolization for data reduction and risky lane change detection,” IEEE Transactions on Intelligent Vehicles , vol. 3, no. 3, pp. 242–253, 2018
2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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2018
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M. Ren, A. Pokrovsky, B. Yang, and R. Urtasun, “SBNet: Sparse blocks network for fast inference,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8711–8720
2018
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2018
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B. Yang, W. Luo, and R. Urtasun, “PIXOR: Real-time 3D object detection from point clouds,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018, pp. 7652–7660
2018
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2018
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2018
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A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “PointPillars: Fast encoders for object detection from point clouds,” Dec. 2018
2018
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2018
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C. M. Martinez, M. Heucke, F.-Y. Wang, B. Gao, and D. Cao, “Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey,” IEEE Transactions on Intelligent Transportation Systems , vol. 19, no. 3, pp. 666–676, 2018
2018
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K. Sama, Y. Morales, N. Akai, H. Liu, E. Takeuchi, and K. Takeda, “Driving feature extraction and behavior classification using an autoencoder to reproduce the velocity styles of experts,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 1337–1343
2018
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2018
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2018
Later among the works it cites.
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2018
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Y. Chen, J. Wang, J. Li, C. Lu, Z. Luo, H. Xue, and C. Wang, “Lidar-video driving dataset: Learning driving policies effectively,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5870–5878
2018
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Y. Choi, N. Kim, S. Hwang, K. Park, J. S. Yoon, K. An, and I. S. Kweon, “KAIST multi-spectral day/night data set for autonomous and assisted driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 19, no. 3, pp. 934–948, 2018
2018
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2018
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2019
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T. B. Lee. Autopilot was active when a tesla crashed into a truck, killing driver. https://arstechnica.com/cars/2019/05/feds-autopilot-was-active-during-deadly-march-tesla-crash/ . [Retrieved May 19, 2019]
2019
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2019
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Deloitte. 2019 deloitte global automotive consumer study – advanced vehicle technologies and multimodal transportation, global focus countries. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/manufacturing/us-global-automotive-consumer-study-2019.pdf . [Retrieved May 19, 2019]
2019
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Federatione Internationale de l’Automobile (FiA) Region 1. The automotive digital transformation and the economic impacts of existing data access models. https://www.fiaregion1.com/wp-content/uploads/2019/03/The-Automotive-Digital-Transformation˙Full-study.pdf . [Retrieved May 19, 2019]
2019
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J. D’Onfro. ‘I hate them’: Locals reportedly are frustrated with alphabet’s self-driving cars. https://www.cnbc.com/2018/08/28/locals-reportedly-frustrated-with-alphabets-waymo-self-driving-cars.html . [Retrieved May 19, 2019]
2019
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Baidu. Apollo auto. https://github.com/ApolloAuto/apollo . [Retrieved May 1, 2019]
2019
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J. Wang, Y. Shao, Y. Ge, and R. Yu, “A survey of vehicle to everything (v2x) testing,” Sensors , vol. 19, no. 2, p. 334, 2019
2019
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T. B. Lee. How 10 leading companies are trying to make powerful, low-cost lidar. https://arstechnica.com/cars/2019/02/the-ars-technica-guide-to-the-lidar-industry/ . [Retrieved May 19, 2019]
2019
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G. Gallego, T. Delbruck, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. Davison, J. Conradt, K. Daniilidis, and D. Scaramuzza, “Event-based vision: A survey,” pre-print , Apr. 2019
2019
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X. Ma, Z. Wang, H. Li, W. Ouyang, and P. Zhang, “Accurate monocular 3D object detection via Color-Embedded 3D reconstruction for autonomous driving,” Mar. 2019
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2019
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E. Yurtsever, Y. Liu, J. Lambert, C. Miyajima, E. Takeuchi, K. Takeda, and J. H. L. Hansen, “Risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , Oct 2019, pp. 3100–3107
2019
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E. Yurtsever, C. Miyajima, and K. Takeda, “A traffic flow simulation framework for learning driver heterogeneity from naturalistic driving data using autoencoders,” International Journal of Automotive Engineering , vol. 10, no. 1, pp. 86–93, 2019
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O. Carsten and M. H. Martens, “How can humans understand their automated cars? hmi principles, problems and solutions,” Cognition, Technology & Work , vol. 21, no. 1, pp. 3–20, 2019
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2039
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