{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T09:16:39Z","timestamp":1770714999723,"version":"3.49.0"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["NeTS-2342833, CAREER-2144505, and MRI-2018966"],"award-info":[{"award-number":["NeTS-2342833, CAREER-2144505, and MRI-2018966"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    We present\n                    <jats:italic toggle=\"yes\">MiHazeFree3D<\/jats:italic>\n                    , a system that leverages millimeter-wave (mmWave) radar signals to predict 3D bounding boxes of vehicles and pedestrians in real-world traffic scenarios. While current 3D object detection methods rely primarily on cameras and LiDARs, their performance degrades significantly in rain, fog, or poor lighting conditions. Our system exploits mmWave radar\u2019s ability to operate reliably in these challenging conditions, offering a complement to existing sensors without increasing computational costs. The key challenge in using mmWave for 3D detection lies in handling motion-induced errors and the specular reflection of mmWave signals. To address these issues, we developed a deep learning architecture with multiple feature fusion layers and trained it on diverse real-world scenarios. We evaluated\n                    <jats:italic toggle=\"yes\">MiHazeFree3D<\/jats:italic>\n                    using data collected from mmWave radars mounted on the dashboard of an ego-vehicle driving through urban environments. Our results show that\n                    <jats:italic toggle=\"yes\">MiHazeFree3D<\/jats:italic>\n                    detects and bounds both vehicles and pedestrians in tested conditions, including fog and low-light scenarios, highlighting the potential of mmWave radar for 3D object detection in autonomous driving systems.\n                  <\/jats:p>","DOI":"10.1145\/3786764","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T21:25:39Z","timestamp":1767129939000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MiHazeFree3D: 3D Bounding Box Prediction for Vehicles and Pedestrians in Fog and Low-Light Conditions"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9353-2011","authenticated-orcid":false,"given":"Hem","family":"Regmi","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina","place":["Columbia, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5044-6938","authenticated-orcid":false,"given":"Reza","family":"Tavasoli","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina","place":["Columbia, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5711-3087","authenticated-orcid":false,"given":"Sanjib","family":"Sur","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina","place":["Columbia, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5990-394X","authenticated-orcid":false,"given":"Srihari","family":"Nelakuditi","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina","place":["Columbia, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"The Weather Channel. 2024. Weather-Related Vehicle Accidents Far More Deadly Than Tornadoes Hurricanes Floods. Retrieved November 15 2024 from https:\/\/weather.com\/safety\/winter\/news\/weather-fatalities-car-crashes-accidents-united-states"},{"key":"e_1_3_2_3_2","unstructured":"U.S. Department of Transportation Federal Highway Administration. 2024. Low Visibility. Retrieved November 15 2024 from https:\/\/ops.fhwa.dot.gov\/weather\/weather_events\/low_visibility.htm"},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the International Conference on Computing and Informatics","author":"Kamioka Yasir Dawood Salman, Ku Ruhana Ku-Mahamud, and Eiji","year":"2017","unstructured":"Yasir Dawood Salman, Ku Ruhana Ku-Mahamud, and Eiji Kamioka. 2017. Distance measurement for self-driving cars using stereo camera. In Proceedings of the International Conference on Computing and Informatics."},{"key":"e_1_3_2_5_2","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Pollefeys Gim Hee Lee, Friedrich Faundorfer, and Marc","year":"2013","unstructured":"Gim Hee Lee, Friedrich Faundorfer, and Marc Pollefeys. 2013. Motion estimation for self-driving cars with a generalized camera. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition."},{"issue":"2","key":"e_1_3_2_6_2","article-title":"An overview of artificial intelligence in automobile industry\u2013a case study on tesla cars","volume":"64","author":"Nagra P. V. Ajitha and Ankita","year":"2021","unstructured":"P. V. Ajitha and Ankita Nagra. 2021. An overview of artificial intelligence in automobile industry\u2013a case study on tesla cars. Solid State Technology 64, 2 (2021), 503\u2013512.","journal-title":"Solid State Technology"},{"key":"e_1_3_2_7_2","volume-title":"Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC)","year":"2020","unstructured":"Amna Shahid and Malaika Mushtaq. 2020. A survey comparing specialized hardware and evolution in TPUs for neural networks. In Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC)."},{"key":"e_1_3_2_8_2","volume-title":"Applications of Artificial Intelligence in Engineering: Proceedings of 1st Global Conference on Artificial Intelligence and Applications (GCAIA 2020)","author":"Gupta Vijeta Sharma, Gaurav Kumar Gupta, and Manjari","year":"2021","unstructured":"Vijeta Sharma, Gaurav Kumar Gupta, and Manjari Gupta. 2021. Performance benchmarking of GPU and TPU on google colaboratory for convolutional neural network. In Applications of Artificial Intelligence in Engineering: Proceedings of 1st Global Conference on Artificial Intelligence and Applications (GCAIA 2020)."},{"key":"e_1_3_2_9_2","volume-title":"Proceedings of the 44th Annual International Symposium on Computer Architecture","author":"al Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et","year":"2017","unstructured":"Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al. 2017. In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture."},{"issue":"2","key":"e_1_3_2_10_2","article-title":"An improved safety algorithm for artificial intelligence enabled processors in self driving cars","volume":"1","author":"Manoharan Samuel","year":"2019","unstructured":"Samuel Manoharan. 2019. An improved safety algorithm for artificial intelligence enabled processors in self driving cars. Journal of Artificial Intelligence 1, 2 (2019), 95\u2013104.","journal-title":"Journal of Artificial Intelligence"},{"issue":"18","key":"e_1_3_2_11_2","article-title":"Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings","volume":"36","author":"Lanz Joo-Wha Hong, Yunwen Wang, and Paulina","year":"2020","unstructured":"Joo-Wha Hong, Yunwen Wang, and Paulina Lanz. 2020. Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human\u2013Computer Interaction 36, 18 (2020), 1768\u20131774.","journal-title":"International Journal of Human\u2013Computer Interaction"},{"key":"e_1_3_2_12_2","volume-title":"Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems","author":"Frtunikj Qing Rao and Jelena","year":"2018","unstructured":"Qing Rao and Jelena Frtunikj. 2018. Deep learning for self-driving cars: Chances and challenges. In Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems."},{"key":"e_1_3_2_13_2","volume-title":"Proceedings of the 2020 4th International Conference on Computer, Communication, and Signal Processing (ICCCSP)","author":"Badgujar Hiral Thadeshwar, Vinit Shah, Mahek Jain, Rujata Chaudhari, and Vishal","year":"2020","unstructured":"Hiral Thadeshwar, Vinit Shah, Mahek Jain, Rujata Chaudhari, and Vishal Badgujar. 2020. Artificial intelligence based self-driving car. In Proceedings of the 2020 4th International Conference on Computer, Communication, and Signal Processing (ICCCSP)."},{"issue":"3","key":"e_1_3_2_14_2","article-title":"Next-generation experimentation with self-driving laboratories","volume":"1","author":"Aspuru-Guzik Florian H\u00e4se, Lo\u00efc M. Roch, and Al\u00e1n","year":"2019","unstructured":"Florian H\u00e4se, Lo\u00efc M. Roch, and Al\u00e1n Aspuru-Guzik. 2019. Next-generation experimentation with self-driving laboratories. Trends in Chemistry 1, 3 (2019), 282\u2013291.","journal-title":"Trends in Chemistry"},{"issue":"1","key":"e_1_3_2_15_2","article-title":"Autonomous driving cars in smart cities: Recent advances, requirements, and challenges","volume":"34","author":"Hong Ibrar Yaqoob, Latif U. Khan, S. M. Ahsan Kazmi, Muhammad Imran, Nadra Guizani, and Choong Seon","year":"2019","unstructured":"Ibrar Yaqoob, Latif U. Khan, S. M. Ahsan Kazmi, Muhammad Imran, Nadra Guizani, and Choong Seon Hong. 2019. Autonomous driving cars in smart cities: Recent advances, requirements, and challenges. IEEE Network 34, 1 (2019), 174\u2013181.","journal-title":"IEEE Network"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Li Li Ding Wen Nan-Ning Zheng and Lin-Cheng Shen 2011. Cognitive cars: A new frontier for ADAS research. IEEE Transactions on Intelligent Transportation Systems 13 1 (2011) 395\u2013407.","DOI":"10.1109\/TITS.2011.2159493"},{"issue":"5","key":"e_1_3_2_17_2","article-title":"High-performance automotive radar: A review of signal processing algorithms and modulation schemes","volume":"36","author":"Yang Gor Hakobyan and Bin","year":"2019","unstructured":"Gor Hakobyan and Bin Yang. 2019. High-performance automotive radar: A review of signal processing algorithms and modulation schemes. IEEE Signal Processing Magazine 36, 5 (2019), 32\u201344.","journal-title":"IEEE Signal Processing Magazine"},{"issue":"10","key":"e_1_3_2_18_2","article-title":"Massive MIMO systems for 5G and beyond networks\u2014overview, recent trends, challenges, and future research direction","volume":"20","author":"Akl Robin Chataut and Robert","year":"2020","unstructured":"Robin Chataut and Robert Akl. 2020. Massive MIMO systems for 5G and beyond networks\u2014overview, recent trends, challenges, and future research direction. Sensors 20, 10, Article 2753 (2020).","journal-title":"Sensors"},{"key":"e_1_3_2_19_2","volume-title":"Proceedings of the 17th European Conference on Computer Vision\u2013ECCV 2022, Tel Aviv, Israel, October 23\u201327, 2022, Part XXXIX","author":"Hassanieh Sohrab Madani, Jayden Guan, Waleed Ahmed, Saurabh Gupta, and Haitham","year":"2022","unstructured":"Sohrab Madani, Jayden Guan, Waleed Ahmed, Saurabh Gupta, and Haitham Hassanieh. 2022. Radatron: Accurate detection Using multi-resolution cascaded MIMO radar. In Proceedings of the 17th European Conference on Computer Vision\u2013ECCV 2022, Tel Aviv, Israel, October 23\u201327, 2022, Part XXXIX."},{"issue":"7","key":"e_1_3_2_20_2","article-title":"Mmwave radar and vision fusion for object detection in autonomous driving: A review","volume":"22","author":"Feng Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, and Zhiyong","year":"2022","unstructured":"Zhiqing Wei, Fengkai Zhang, Shuo Chang, Yangyang Liu, Huici Wu, and Zhiyong Feng. 2022. Mmwave radar and vision fusion for object detection in autonomous driving: A review. Sensors 22, 7, Article 2542 (2022).","journal-title":"Sensors"},{"issue":"8","key":"e_1_3_2_21_2","article-title":"MIMO-SAR: A hierarchical high-resolution imaging algorithm for mmWave FMCW radar in autonomous driving","volume":"70","author":"Xing Xiangyu Gao, Sumit Roy, and Guanbin","year":"2021","unstructured":"Xiangyu Gao, Sumit Roy, and Guanbin Xing. 2021. MIMO-SAR: A hierarchical high-resolution imaging algorithm for mmWave FMCW radar in autonomous driving. IEEE Transactions on Vehicular Technology 70, 8 (2021), 7322\u20137334.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_2_22_2","volume-title":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","author":"Bharadia Kshitiz Bansal, Keshav Rungta, Siyuan Zhu, and Dinesh","year":"2020","unstructured":"Kshitiz Bansal, Keshav Rungta, Siyuan Zhu, and Dinesh Bharadia. 2020. Pointillism: Accurate 3d bounding box estimation with multi-radars. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems."},{"key":"e_1_3_2_23_2","unstructured":"Texas Instrument. 2024. MMWCAS-DSP-EVM. Retrieved November 15 2024 from https:\/\/www.ti.com\/tool\/MMWCAS-DSP-EVM"},{"key":"e_1_3_2_24_2","unstructured":"Texas Instrument. 2024. MMWCAS-RF-EVM. Retrieved November 15 2024 from https:\/\/www.ti.com\/tool\/MMWCAS-RF-EVM"},{"key":"e_1_3_2_25_2","unstructured":"Stereo Labs. 2024. ZED 2. Retrieved November 15 2024 from https:\/\/www.stereolabs.com\/zed-2\/"},{"key":"e_1_3_2_26_2","unstructured":"Ouster. 2024. Velodyne LiDAR An Ouster Company. Retrieved November 15 2024 from https:\/\/velodyneLiDAR.com\/products\/puck\/"},{"issue":"6","key":"e_1_3_2_27_2","article-title":"Application of linear-frequency-modulated continuous-wave (LFMCW) radars for tracking of vital signs","volume":"62","author":"Gomez-Garcia Guochao Wang, Jose-Maria Munoz-Ferreras, Changzhan Gu, Changzhi Li, and Roberto","year":"2014","unstructured":"Guochao Wang, Jose-Maria Munoz-Ferreras, Changzhan Gu, Changzhi Li, and Roberto Gomez-Garcia. 2014. Application of linear-frequency-modulated continuous-wave (LFMCW) radars for tracking of vital signs. IEEE Transactions on Microwave Theory and Techniques 62, 6 (2014), 1387\u20131399.","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"9","key":"e_1_3_2_28_2","article-title":"Quantitative gait measurement with pulse-doppler radar for passive in-home gait assessment","volume":"61","author":"Cuddihy Fang Wang, Marjorie Skubic, Marilyn Rantz, and Paul E.","year":"2014","unstructured":"Fang Wang, Marjorie Skubic, Marilyn Rantz, and Paul E. Cuddihy. 2014. Quantitative gait measurement with pulse-doppler radar for passive in-home gait assessment. IEEE Transactions on Biomedical Engineering 61, 9 (2014), 2434\u20132443.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"3","key":"e_1_3_2_29_2","article-title":"Squigglemilli: Approximating sar imaging on mobile millimeter-wave devices","volume":"5","author":"Nelakuditi Hem Regmi, Moh Sabbir Saadat, Sanjib Sur, and Srihari","year":"2021","unstructured":"Hem Regmi, Moh Sabbir Saadat, Sanjib Sur, and Srihari Nelakuditi. 2021. Squigglemilli: Approximating sar imaging on mobile millimeter-wave devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 3 (2021), 125:1\u2013125:26.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_30_2","unstructured":"Stereo Labs. 2025. 3D Object Detection Overview. Retrieved November 15 2024 from https:\/\/www.stereolabs.com\/docs\/object-detection"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22093332"},{"issue":"8","key":"e_1_3_2_32_2","article-title":"CenterNet3D: An anchor free object detector for point cloud","volume":"23","author":"Cao Guojun Wang, Jian Wu, Bin Tian, Siyu Teng, Long Chen, and Dongpu","year":"2021","unstructured":"Guojun Wang, Jian Wu, Bin Tian, Siyu Teng, Long Chen, and Dongpu Cao. 2021. CenterNet3D: An anchor free object detector for point cloud. IEEE Transactions on Intelligent Transportation Systems 23, 8 (2021), 12953\u201312965.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_33_2","volume-title":"Beamforming: Sensor Signal Processing for Defence Applications","author":"Manikas Athanassios","year":"2014","unstructured":"Athanassios Manikas. 2014. Beamforming: Sensor Signal Processing for Defence Applications. Imperial College Press."},{"key":"e_1_3_2_34_2","unstructured":"Ultralytics. 2024. Neural Network Blocks. Retrieved November 15 2024 from https:\/\/github.com\/ultralytics\/ultralytics\/tree\/main\/ultralytics\/nn"},{"key":"e_1_3_2_35_2","volume-title":"Proceedings of the 2021 International Conference on Disruptive Technologies for Multi-disciplinary Research and Applications (CENTCON)","author":"Agarwal Sheldon Mascarenhas and Mukul","year":"2021","unstructured":"Sheldon Mascarenhas and Mukul Agarwal. 2021. A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for image classification. In Proceedings of the 2021 International Conference on Disruptive Technologies for Multi-disciplinary Research and Applications (CENTCON)."},{"key":"e_1_3_2_36_2","volume-title":"Proceedings of the 2017 2nd International Conference on Image, Vision and Computing (ICIVC)","author":"Nan Xiaoling Xia, Cui Xu, and Bing","year":"2017","unstructured":"Xiaoling Xia, Cui Xu, and Bing Nan. 2017. Inception-v3 for flower classification. In Proceedings of the 2017 2nd International Conference on Image, Vision and Computing (ICIVC)."},{"key":"e_1_3_2_37_2","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","year":"2021","unstructured":"Zixiao Zhang, Xiaoqiang Lu, Guojin Cao, Yuting Yang, Licheng Jiao, and Fang Liu 2021. ViT-YOLO: Transformer-based YOLO for object detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision."},{"key":"e_1_3_2_38_2","unstructured":"Chigozie Nwankpa Winifred Ijomah Anthony Gachagan and Stephen Marshall. 2018. Activation functions: Comparison of trends in practice and research for deep learning. arXiv:1811.03378. Retrieved from https:\/\/arxiv.org\/abs\/1811.03378"},{"key":"e_1_3_2_39_2","article-title":"Foveabox: Beyound anchor-based object detection","volume":"29","author":"Shi Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, Lei Li, and Jianbo","year":"2020","unstructured":"Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, Lei Li, and Jianbo Shi. 2020. Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing 29 (2020), 7389\u20137398.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_2_40_2","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Huang Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, and Dong","year":"2020","unstructured":"Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, and Dong Huang. 2020. Multiple anchor learning for visual object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"issue":"6","key":"e_1_3_2_41_2","article-title":"Learning to match anchors for visual object detection","volume":"44","author":"Ye Xiaosong Zhang, Fang Wang, Chang Liu, Xiangyang Ji, and Qixiang","year":"2021","unstructured":"Xiaosong Zhang, Fang Wang, Chang Liu, Xiangyang Ji, and Qixiang Ye. 2021. Learning to match anchors for visual object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 6 (2021), 3096\u20133109.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"e_1_3_2_42_2","article-title":"FCOS: A simple and strong anchor-free object detector","volume":"44","author":"He Zhi Tian, Chunhua Shen, Hao Chen, and Tong","year":"2020","unstructured":"Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. 2020. FCOS: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 4 (2020), 1922\u20131933.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_43_2","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Greenspan Mohsen Zand, Ali Etemad, and Michael","year":"2022","unstructured":"Mohsen Zand, Ali Etemad, and Michael Greenspan. 2022. Objectbox: From centers to boxes for anchor-free object detection. In Proceedings of the European Conference on Computer Vision."},{"key":"e_1_3_2_44_2","unstructured":"Texas Instruments. 2024. MMWAVE-STUDIO. Retrieved November 15 2024 from https:\/\/www.ti.com\/tool\/MMWAVE-STUDIO"},{"key":"e_1_3_2_45_2","unstructured":"Open-Source. 2024. Adam Pytorch. Retrieved November 15 2024 from https:\/\/pytorch.org\/docs\/stable\/generated\/torch.optim.Adam.html"},{"key":"e_1_3_2_46_2","unstructured":"Open-Source. 2024. Python 3.8. Retrieved November 15 2024 from https:\/\/www.python.org\/downloads\/release\/python-380\/"},{"key":"e_1_3_2_47_2","unstructured":"Open-Source. 2024. PyTorch. Retrieved November 15 2024 from https:\/\/pytorch.org\/"},{"key":"e_1_3_2_48_2","unstructured":"NVIDIA. 2024. RTX A6000. Retrieved November 15 2024 from https:\/\/www.nvidia.com\/en-us\/design-visualization\/rtx-a6000\/"},{"key":"e_1_3_2_49_2","article-title":"Pelee: A real-time object detection system on mobile devices","volume":"31","author":"Ling Robert J. Wang, Xiang Li, and Charles X.","year":"2018","unstructured":"Robert J. Wang, Xiang Li, and Charles X. Ling. 2018. Pelee: A real-time object detection system on mobile devices. Advances in Neural Information Processing Systems 31 (2018), 1967\u20131976.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","author":"Silva Rafael Padilla, Sergio L. Netto, and Eduardo A. B. Da","year":"2020","unstructured":"Rafael Padilla, Sergio L. Netto, and Eduardo A. B. Da Silva. 2020. A survey on performance metrics for object-detection algorithms. In Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP)."},{"key":"e_1_3_2_51_2","article-title":"Precision-recall-gain curves: PR analysis done right","volume":"28","author":"Kull Peter Flach and Meelis","year":"2015","unstructured":"Peter Flach and Meelis Kull. 2015. Precision-recall-gain curves: PR analysis done right. Advances in Neural Information Processing Systems 28 (2015), 838\u2013846.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_52_2","unstructured":"Computer History Museum. 2024. Timeline of Computer History. Retrieved November 15 2024 from https:\/\/www.computerhistory.org\/timeline\/memory-storage\/"},{"key":"e_1_3_2_53_2","unstructured":"Marie Yahiaoui Hazem Rashed Letizia Mariotti Ganesh Sistu Ian Clancy Lucie Yahiaoui Varun Ravi Kumar and Senthil Yogamani. 2019. Fisheyemodnet: Moving object detection on surround-view cameras for autonomous driving. arXiv:1908.11789. Retrieved from https:\/\/arxiv.org\/abs\/1908.11789"},{"issue":"5","key":"e_1_3_2_54_2","article-title":"Advanced driver-assistance systems: A path toward autonomous vehicles","volume":"7","author":"Bradley Vipin Kumar Kukkala, Jordan Tunnell, Sudeep Pasricha, and Thomas","year":"2018","unstructured":"Vipin Kumar Kukkala, Jordan Tunnell, Sudeep Pasricha, and Thomas Bradley. 2018. Advanced driver-assistance systems: A path toward autonomous vehicles. IEEE Consumer Electronics Magazine 7, 5 (2018), 18\u201325.","journal-title":"IEEE Consumer Electronics Magazine"},{"issue":"2","key":"e_1_3_2_55_2","article-title":"Opportunities and challenges of mmWave NR","volume":"26","author":"Tang Aida Vera Lopez, Andrey Chervyakov, Greg Chance, Sumit Verma, and Yang","year":"2019","unstructured":"Aida Vera Lopez, Andrey Chervyakov, Greg Chance, Sumit Verma, and Yang Tang. 2019. Opportunities and challenges of mmWave NR. IEEE Wireless Communications 26, 2 (2019), 4\u20136.","journal-title":"IEEE Wireless Communications"},{"issue":"4","key":"e_1_3_2_56_2","article-title":"RODNet: A real-time radar object detection network cross-supervised by camera-radar fused object 3D localization","volume":"15","author":"Liu Yizhou Wang, Zhongyu Jiang, Yudong Li, Jenq-Neng Hwang, Guanbin Xing, and Hui","year":"2021","unstructured":"Yizhou Wang, Zhongyu Jiang, Yudong Li, Jenq-Neng Hwang, Guanbin Xing, and Hui Liu. 2021. RODNet: A real-time radar object detection network cross-supervised by camera-radar fused object 3D localization. IEEE Journal of Selected Topics in Signal Processing 15, 4 (2021), 954\u2013967.","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"e_1_3_2_57_2","unstructured":"Dong-Hee Paek Seung-Hyun Kong and Kevin Tirta Wijaya. 2022. K-Radar: 4D radar object detection for autonomous driving in various weather conditions. arXiv:2206.08171. Retrieved from https:\/\/arxiv.org\/abs\/2206.08171"},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","unstructured":"Jen-Hao Cheng Sheng-Yao Kuan Hugo Latapie Gaowen Liu and Jenq-Neng Hwang. 2023. CenterRadarNet: Joint 3D object detection and tracking framework using 4D FMCW radar. arXiv:2311.01423. Retrieved from https:\/\/arxiv.org\/abs\/2311.01423","DOI":"10.1109\/ICIP51287.2024.10648077"},{"key":"e_1_3_2_59_2","article-title":"Graph convolutional networks for 3D object detection on radar data","author":"Tomforde Michael Meyer, Georg Kuschk, and Sven","year":"2021","unstructured":"Michael Meyer, Georg Kuschk, and Sven Tomforde. 2021. Graph convolutional networks for 3D object detection on radar data. In Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW).","journal-title":"In Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)."},{"key":"e_1_3_2_60_2","volume-title":"Proceedings of the 2012 6th European Conference on Antennas and Propagation (EUCAP)","author":"Stelzer Christian M. Schmid, Reinhard Feger, Clemens Pfeffer, and Andreas","year":"2012","unstructured":"Christian M. Schmid, Reinhard Feger, Clemens Pfeffer, and Andreas Stelzer. 2012. Motion compensation and efficient array design for TDMA FMCW MIMO radar systems. In Proceedings of the 2012 6th European Conference on Antennas and Propagation (EUCAP)."},{"key":"e_1_3_2_61_2","volume-title":"Proceedings of the 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","author":"Sur Jacqueline M. Schellberg, Hem Regmi, and Sanjib","year":"2023","unstructured":"Jacqueline M. Schellberg, Hem Regmi, and Sanjib Sur. 2023. mmSight: Towards robust millimeter-wave imaging on handheld devices. In Proceedings of the 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)."},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21165397"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23218901"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.5194\/ars-3-205-2005"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20226532"}],"container-title":["ACM Transactions on Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3786764","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3786764","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:21:13Z","timestamp":1770643273000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3786764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2,28]]}},"alternative-id":["10.1145\/3786764"],"URL":"https:\/\/doi.org\/10.1145\/3786764","relation":{},"ISSN":["2691-1914","2577-6207"],"issn-type":[{"value":"2691-1914","type":"print"},{"value":"2577-6207","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]},"assertion":[{"value":"2024-12-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}