Fetching the paper…
Reading the bibliography…
Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs.
A. Geiger, F. Moosmann, Ö. Car, and B. Schuster, “Automatic camera and range sensor calibration using a single shot,” in 2012 IEEE international conference on robotics and automation . IEEE, 2012, pp. 3936–3943
2012
Earlier work this paper cites.
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun, “3d object proposals for accurate object class detection,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2147–2156
2016
Earlier work this paper cites.
A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka, “3d bounding box estimation using deep learning and geometry,” pp. 7074–7082, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Stolz, M. Wolf, F. Meinl, M. Kunert, and W. Menzel, “A new antenna array and signal processing concept for an automotive 4d radar,” in 2018 15th European Radar Conference (EuRAD) . IEEE, 2018, pp. 63–66
2018
Earlier work this paper cites.
H. Wang, Y. Huang, A. Soltani, A. Khajepour, and D. Cao, “Cyber-physical predictive energy management for through-the-road hybrid vehicles,” IEEE Transactions on Vehicular Technology , vol. 68, no. 4, pp. 3246–3256, 2019
2019
Earlier work this paper cites.
Y. Huang, H. Wang, A. Khajepour, H. Ding, K. Yuan, and Y. Qin, “A novel local motion planning framework for autonomous vehicles based on resistance network and model predictive control,” IEEE Transactions on Vehicular Technology , vol. 69, no. 1, pp. 55–66, 2019
2019
Earlier work this paper cites.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A survey on 3d object detection methods for autonomous driving applications,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 10, pp. 3782–3795, 2019
2019
Earlier work this paper cites.
M. Herzog and K. Dietmayer, “Training a fast object detector for lidar range images using labeled data from sensors with higher resolution,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 2707–2713
2019
Earlier work this paper cites.
A. Danzer, T. Griebel, M. Bach, and K. Dietmayer, “2d car detection in radar data with pointnets,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 61–66
2019
Earlier work this paper cites.
M. Meyer and G. Kuschk, “Automotive radar dataset for deep learning based 3d object detection,” in 2019 16th european radar conference (EuRAD) . IEEE, 2019, pp. 129–132
2019
Earlier work this paper cites.
H. Rashed, M. Ramzy, V. Vaquero, A. El Sallab, G. Sistu, and S. Yogamani, “Fusemodnet: Real-time camera and lidar based moving object detection for robust low-light autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
H. Xue, H. Fu, R. Ren, T. Wu, and B. Dai, “Real-time 3d grid map building for autonomous driving in dynamic environment,” in 2019 IEEE International Conference on Unmanned Systems (ICUS) . IEEE, 2019, pp. 40–45
2019
Earlier work this paper cites.
K. Yoneda, N. Suganuma, R. Yanase, and M. Aldibaja, “Automated driving recognition technologies for adverse weather conditions,” IATSS research , vol. 43, no. 4, pp. 253–262, 2019
2019
Earlier work this paper cites.
S. Zang, M. Ding, D. Smith, P. Tyler, T. Rakotoarivelo, and M. A. Kaafar, “The impact of adverse weather conditions on autonomous vehicles: How rain, snow, fog, and hail affect the performance of a self-driving car,” IEEE vehicular technology magazine , vol. 14, no. 2, pp. 103–111, 2019
2019
Earlier work this paper cites.
J. Peršić, I. Marković, and I. Petrović, “Extrinsic 6dof calibration of a radar–lidar–camera system enhanced by radar cross section estimates evaluation,” Robotics and Autonomous Systems , vol. 114, pp. 217–230, 2019
2019
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
H. Wang, Y. Huang, A. Khajepour, D. Cao, and C. Lv, “Ethical decision-making platform in autonomous vehicles with lexicographic optimization based model predictive controller,” IEEE transactions on vehicular technology , vol. 69, no. 8, pp. 8164–8175, 2020
2020
Earlier work this paper cites.
S. Wen, J. Chen, F. R. Yu, F. Sun, Z. Wang, and S. Fan, “Edge computing-based collaborative vehicles 3d mapping in real time,” IEEE Transactions on Vehicular Technology , vol. 69, no. 11, pp. 12 470–12 481, 2020
2020
Cited alongside, same era.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Cited alongside, same era.
K. Bansal, K. Rungta, S. Zhu, and D. Bharadia, “Pointillism: Accurate 3d bounding box estimation with multi-radars,” in Proceedings of the 18th Conference on Embedded Networked Sensor Systems , 2020, pp. 340–353
2020
Cited alongside, same era.
M. Mostajabi, C. M. Wang, D. Ranjan, and G. Hsyu, “High-resolution radar dataset for semi-supervised learning of dynamic objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 100–101
J. Wu, Y. Guo, C. Deng, A. Zhang, H. Qiao, Z. Lu, J. Xie, L. Fang, and Q. Dai, “An integrated imaging sensor for aberration-corrected 3d photography,” Nature , vol. 612, no. 7938, pp. 62–71, 2022
2022
Later among the works it cites.
B. Tan, Z. Ma, X. Zhu, S. Li, L. Zheng, S. Chen, L. Huang, and J. Bai, “3d object detection for multi-frame 4d automotive millimeter-wave radar point cloud,” IEEE Sensors Journal , 2022
2022
Later among the works it cites.
Y. Zhou, L. Liu, H. Zhao, M. López-Benítez, L. Yu, and Y. Yue, “Towards deep radar perception for autonomous driving: Datasets, methods, and challenges,” Sensors , vol. 22, no. 11, p. 4208, 2022
2022
Later among the works it cites.
L. Wang, X. Zhang, J. Li, B. Xv, R. Fu, H. Chen, L. Yang, D. Jin, and L. Zhao, “Multi-modal and multi-scale fusion 3d object detection of 4d radar and lidar for autonomous driving,” IEEE Transactions on Vehicular Technology , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, and F. Heide, “Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 682–11 692
2020
Cited alongside, same era.
F. Secci and A. Ceccarelli, “On failures of rgb cameras and their effects in autonomous driving applications,” in 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2020, pp. 13–24
2020
Cited alongside, same era.
M. Sheeny, E. De Pellegrin, S. Mukherjee, A. Ahrabian, S. Wang, and A. Wallace, “Radiate: A radar dataset for automotive perception in bad weather,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 1–7
2021
Cited alongside, same era.
J.-L. Déziel, P. Merriaux, F. Tremblay, D. Lessard, D. Plourde, J. Stanguennec, P. Goulet, and P. Olivier, “Pixset: An opportunity for 3d computer vision to go beyond point clouds with a full-waveform lidar dataset,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 2987–2993
2021
Cited alongside, same era.
O. Schumann, M. Hahn, N. Scheiner, F. Weishaupt, J. F. Tilly, J. Dickmann, and C. Wöhler, “Radarscenes: A real-world radar point cloud data set for automotive applications,” in 2021 IEEE 24th International Conference on Information Fusion (FUSION) . IEEE, 2021, pp. 1–8
2021
Cited alongside, same era.
J. Vargas, S. Alsweiss, O. Toker, R. Razdan, and J. Santos, “An overview of autonomous vehicles sensors and their vulnerability to weather conditions,” Sensors , vol. 21, no. 16, p. 5397, 2021
2021
Cited alongside, same era.
B. Xu, X. Zhang, L. Wang, X. Hu, Z. Li, S. Pan, J. Li, and Y. Deng, “Rpfa-net: A 4d radar pillar feature attention network for 3d object detection,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 3061–3066
2021
Cited alongside, same era.
F. J. Abdu, Y. Zhang, M. Fu, Y. Li, and Z. Deng, “Application of deep learning on millimeter-wave radar signals: A review,” Sensors , vol. 21, no. 6, p. 1951, 2021
2021
Cited alongside, same era.
A. Kramer, K. Harlow, C. Williams, and C. Heckman, “Coloradar: The direct 3d millimeter wave radar dataset,” The International Journal of Robotics Research , vol. 41, no. 4, pp. 351–360, 2022
2022
Later among the works it cites.
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X.-S. Hua, M.-J. Zhao, and G. H. Lee, “Rethinking iou-based optimization for single-stage 3d object detection,” in European Conference on Computer Vision . Springer, 2022, pp. 544–561
2022
Later among the works it cites.
H. Wu, J. Deng, C. Wen, X. Li, C. Wang, and J. Li, “Casa: A cascade attention network for 3-d object detection from lidar point clouds,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022
2022
Later among the works it cites.
Y. Li, X. Qi, Y. Chen, L. Wang, Z. Li, J. Sun, and J. Jia, “Voxel field fusion for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1120–1129
2022
Later among the works it cites.
Z. Song, C. Jia, L. Yang, H. Wei, and L. Liu, “Graphalign++: An accurate feature alignment by graph matching for multi-modal 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 2023
2023
Closest in time.
J. Wang, R. Li, X. Zhang, and Y. He, “Interference mitigation for automotive fmcw radar based on contrastive learning with dilated convolution,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Closest in time.
L. Wang, X. Zhang, Z. Song, J. Bi, G. Zhang, H. Wei, L. Tang, L. Yang, J. Li, C. Jia, and L. Zhao, “Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 7, pp. 3781–3798, 2023
2023
Closest in time.
Z. Song, H. Wei, C. Jia, Y. Xia, X. Li, and C. Zhang, “Vp-net: Voxels as points for 3-d object detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–12, 2023
2023
Closest in time.
L. Yang, K. Yu, T. Tang, J. Li, K. Yuan, L. Wang, X. Zhang, and P. Chen, “Bevheight: A robust framework for vision-based roadside 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 21 611–21 620
2023
Closest in time.
L. Yang, X. Zhang, J. Li, L. Wang, M. Zhu, C. Zhang, and H. Liu, “Mix-teaching: A simple, unified and effective semi-supervised learning framework for monocular 3d object detection,” IEEE Transactions on Circuits and Systems for Video Technology , pp. 1–1, 2023
2023
Closest in time.
L. Wang, X. Zhang, W. Qin, X. Li, J. Gao, L. Yang, Z. Li, J. Li, L. Zhu, H. Wang et al. , “Camo-mot: Combined appearance-motion optimization for 3d multi-object tracking with camera-lidar fusion,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Closest in time.
L. Zheng, S. Li, B. Tan, L. Yang, S. Chen, L. Huang, J. Bai, X. Zhu, and Z. Ma, “Rcfusion: Fusing 4d radar and camera with bird’s-eye view features for 3d object detection,” IEEE Transactions on Instrumentation and Measurement , 2023
2023
Closest in time.
M. Jiang, G. Xu, H. Pei, Z. Feng, S. Ma, H. Zhang, and W. Hong, “4d high-resolution imagery of point clouds for automotive mmwave radar,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Closest in time.
G. El Natour, O. A. Aider, R. Rouveure, F. Berry, and P. Faure, “Radar and vision sensors calibration for outdoor 3d reconstruction,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 2084–2089
2089
Closest in time.