{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T01:14:01Z","timestamp":1771636441753,"version":"3.50.1"},"reference-count":220,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20272"],"award-info":[{"award-number":["U23A20272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2069"],"award-info":[{"award-number":["U22A2069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272146"],"award-info":[{"award-number":["62272146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["252300421237"],"award-info":[{"award-number":["252300421237"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Special projects of Henan Province","award":["251111210900"],"award-info":[{"award-number":["251111210900"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["252102211014"],"award-info":[{"award-number":["252102211014"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s44443-025-00302-0","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T08:38:53Z","timestamp":1763627933000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advances in object detection for autonomous driving using mmwave radar and camera: A comprehensive survey"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1123-6978","authenticated-orcid":false,"given":"Kaikai","family":"Deng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-3817","authenticated-orcid":false,"given":"Ling","family":"Xing","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0209-4488","authenticated-orcid":false,"given":"Honghai","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0291-3001","authenticated-orcid":false,"given":"Huahong","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8656-5134","authenticated-orcid":false,"given":"Yue","family":"Ling","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7950-9835","authenticated-orcid":false,"given":"Jianping","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"302_CR1","doi-asserted-by":"crossref","unstructured":"Ahuja K, Jiang Y, Goel M et\u00a0al (2021) Vid2doppler: Synthesizing doppler radar data from videos for training privacy-preserving activity recognition. In: Proc. of CHI, pp 1\u201310","DOI":"10.1145\/3411764.3445138"},{"issue":"5","key":"302_CR2","first-page":"1","volume":"55","author":"M AlAmir","year":"2022","unstructured":"AlAmir M, AlGhamdi M (2022) The role of generative adversarial network in medical image analysis: an in-depth survey. ACM CSUR 55(5):1\u201336","journal-title":"ACM CSUR"},{"key":"302_CR3","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G et\u00a0al (2021) Vivit: A video vision transformer. In: Proc. of IEEE ICCV, pp 6836\u20136846","DOI":"10.1109\/ICCV48922.2021.00676"},{"issue":"3","key":"302_CR4","first-page":"1852","volume":"23","author":"E Arnold","year":"2020","unstructured":"Arnold E, Dianati M, de Temple R et al (2020) Cooperative perception for 3d object detection in driving scenarios using infrastructure sensors. IEEE TITS 23(3):1852\u20131864","journal-title":"IEEE TITS"},{"key":"302_CR5","unstructured":"Barbosa FM, Os\u00f3rio FS (2023) Camera-radar perception for autonomous vehicles and adas: Concepts, datasets and metrics. arXiv:2303.04302"},{"key":"302_CR6","doi-asserted-by":"crossref","unstructured":"Beery S, Wu G, Rathod V et\u00a0al (2020) Context r-cnn: Long term temporal context for per-camera object detection. In: Proc. of IEEE CVPR, pp 13075\u201313085","DOI":"10.1109\/CVPR42600.2020.01309"},{"key":"302_CR7","doi-asserted-by":"crossref","unstructured":"Bhuyan S, Kar A, Sen D et\u00a0al (2024) Rgb-d fusion through zero-shot fuzzy membership learning for salient object detection. IEEE TAI","DOI":"10.1109\/TAI.2024.3376640"},{"key":"302_CR8","doi-asserted-by":"crossref","unstructured":"Bijelic M, Gruber T, Mannan F et\u00a0al (2020) Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In: Proc. of IEEE CVPR, pp 11682\u201311692","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"302_CR9","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv:2004.10934"},{"issue":"2","key":"302_CR10","first-page":"1","volume":"54","author":"A Boukerche","year":"2021","unstructured":"Boukerche A, Hou Z (2021) Object detection using deep learning methods in traffic scenarios. ACM CSUR 54(2):1\u201335","journal-title":"ACM CSUR"},{"issue":"1\u20132","key":"302_CR11","first-page":"33","volume":"42","author":"K Burnett","year":"2023","unstructured":"Burnett K, Yoon DJ, Wu Y et al (2023) Boreas: a multi-season autonomous driving dataset. IJRR 42(1\u20132):33\u201342","journal-title":"IJRR"},{"key":"302_CR12","doi-asserted-by":"crossref","unstructured":"Caesar H, Bankiti V, Lang AH et\u00a0al (2020) nuscenes: A multimodal dataset for autonomous driving. In: Proc. of IEEE CVPR, pp 11621\u201311631","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"302_CR13","unstructured":"Cai Z, Liu S, Wang G et\u00a0al (2023) Align-detr: Improving detr with simple iou-aware bce loss. arXiv:2304.07527"},{"key":"302_CR14","doi-asserted-by":"crossref","unstructured":"Cao X, Wang P, Zhang Z et\u00a0al (2025) Rafdet: A novel camera-radar fusion framework for robust 3d object detection in autonomous driving. In: Proc. of IEEE ICASSP, IEEE, pp 1\u20135","DOI":"10.1109\/ICASSP49660.2025.10888004"},{"key":"302_CR15","doi-asserted-by":"crossref","unstructured":"Cao Z, Cheng Y, Hu Y et\u00a0al (2024) Using physical dynamics: Accurate and real-time object detection for high-resolution video streaming on internet of things devices. IEEE IoTJ","DOI":"10.1109\/JIOT.2024.3382395"},{"key":"302_CR16","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G et\u00a0al (2020) End-to-end object detection with transformers. In: Proc. of Springer ECCV, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"302_CR17","doi-asserted-by":"crossref","unstructured":"Chadwick S, Maddern W, Newman P (2019) Distant vehicle detection using radar and vision. In: Proc. of IEEE ICRA, pp 8311\u20138317","DOI":"10.1109\/ICRA.2019.8794312"},{"key":"302_CR18","doi-asserted-by":"crossref","unstructured":"Chaney K, Cladera F, Wang Z et\u00a0al (2023) M3ed: Multi-robot, multi-sensor, multi-environment event dataset. In: Proc. of IEEE CVPR, pp 4015\u20134022","DOI":"10.1109\/CVPRW59228.2023.00419"},{"key":"302_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100310","volume":"38","author":"MN Chapel","year":"2020","unstructured":"Chapel MN, Bouwmans T (2020) Moving objects detection with a moving camera: a comprehensive review. Comput Sci Rev 38:100310","journal-title":"Comput Sci Rev"},{"key":"302_CR20","doi-asserted-by":"crossref","unstructured":"Chen G, Wang Q, Dong B et\u00a0al (2024) : Edge-aware multimodal transformer for rgb-d salient object detection. IEEE TNNLS","DOI":"10.2139\/ssrn.4713350"},{"issue":"6","key":"302_CR21","first-page":"3234","volume":"22","author":"L Chen","year":"2021","unstructured":"Chen L, Lin S, Lu X et al (2021) Deep neural network based vehicle and pedestrian detection for autonomous driving: a survey. IEEE TITS 22(6):3234\u20133246","journal-title":"IEEE TITS"},{"key":"302_CR22","doi-asserted-by":"crossref","unstructured":"Chen Y, Yuan X, Wang J et\u00a0al (2025) Yolo-ms: rethinking multi-scale representation learning for real-time object detection. IEEE TPAML","DOI":"10.1109\/TPAMI.2025.3538473"},{"key":"302_CR23","doi-asserted-by":"crossref","unstructured":"Cheng X, Zhou J, Song J et\u00a0al (2023) A highway traffic image enhancement algorithm based on improved gan in complex weather conditions. IEEE TITS","DOI":"10.1109\/TITS.2023.3258063"},{"key":"302_CR24","doi-asserted-by":"crossref","unstructured":"Cheng Y, Zhu J, Jiang M et\u00a0al (2021) Flow: A dataset and benchmark for floating waste detection in inland waters. In: Proc. of IEEE ICCV, pp 10953\u201310962","DOI":"10.1109\/ICCV48922.2021.01077"},{"key":"302_CR25","doi-asserted-by":"crossref","unstructured":"Chu X, Deng J, You G et\u00a0al (2025) Racformer: Towards high-quality 3d object detection via query-based radar-camera fusion. In: Proc. of IEEE CVPR, pp 17081\u201317091","DOI":"10.1109\/CVPR52734.2025.01592"},{"key":"302_CR26","unstructured":"contributors W (2023) Image sensor \u2013 wikipedia, the free encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Image sensor&oldid=1146373856"},{"key":"302_CR27","doi-asserted-by":"crossref","unstructured":"Cui H, Wu J, Zhang J et\u00a0al (2021a) 3d detection and tracking for on-road vehicles with a monovision camera and dual low-cost 4d mmwave radars. In: Proc. of IEEE ITSC, pp 2931\u20132937","DOI":"10.1109\/ITSC48978.2021.9564904"},{"key":"302_CR28","unstructured":"Cui H, Zhong S, Wu J et\u00a0al (2024) Milipoint: A point cloud dataset for mmwave radar. Proc of NIPS 36"},{"issue":"2","key":"302_CR29","first-page":"722","volume":"23","author":"Y Cui","year":"2021","unstructured":"Cui Y, Chen R, Chu W et al (2021) Deep learning for image and point cloud fusion in autonomous driving: a review. IEEE TITS 23(2):722\u2013739","journal-title":"IEEE TITS"},{"issue":"3","key":"302_CR30","first-page":"1","volume":"6","author":"K Deng","year":"2022","unstructured":"Deng K, Zhao D, Han Q et al (2022) Geryon: edge assisted real-time and robust object detection on drones via mmwave radar and camera fusion. Proc of ACM IMWUT 6(3):1\u201327","journal-title":"Proc of ACM IMWUT"},{"key":"302_CR31","doi-asserted-by":"crossref","unstructured":"Deng K, Zhao D, Han Q et\u00a0al (2022b) Global-local feature enhancement network for robust object detection using mmwave radar and camera. In: Proc. of IEEE ICASSP, pp 4708\u20134712","DOI":"10.1109\/ICASSP43922.2022.9746764"},{"issue":"1","key":"302_CR32","first-page":"1","volume":"7","author":"K Deng","year":"2023","unstructured":"Deng K, Zhao D, Han Q et al (2023) Midas: generating mmwave radar data from videos for training pervasive and privacy-preserving human sensing tasks. Proc of ACM IMWUT 7(1):1\u201326","journal-title":"Proc of ACM IMWUT"},{"key":"302_CR33","doi-asserted-by":"crossref","unstructured":"Deng K, Zhao D, Zhang Z et\u00a0al (2023b) Midas++: Generating training data of mmwave radars from videos for privacy-preserving human sensing with mobility. IEEE TMC","DOI":"10.1145\/3580872"},{"key":"302_CR34","doi-asserted-by":"crossref","unstructured":"Diehl C, Feicho E, Schwambach A et\u00a0al (2020) Radar-based dynamic occupancy grid mapping and object detection. In: Proc. of IEEE ITSC, pp 1\u20136","DOI":"10.1109\/ITSC45102.2020.9294626"},{"issue":"3","key":"302_CR35","first-page":"571","volume":"6","author":"J Domhof","year":"2021","unstructured":"Domhof J, Kooij JF, Gavrila DM (2021) A joint extrinsic calibration tool for radar, camera and lidar. IEEE TIV 6(3):571\u2013582","journal-title":"IEEE TIV"},{"key":"302_CR36","doi-asserted-by":"crossref","unstructured":"Dong X, Wang P, Zhang P et\u00a0al (2020) Probabilistic oriented object detection in automotive radar. In: Proc. of IEEE CVPR Workshops, pp 102\u2013103","DOI":"10.1109\/CVPRW50498.2020.00059"},{"key":"302_CR37","doi-asserted-by":"crossref","unstructured":"Dong X, Zhuang B, Mao Y et\u00a0al (2021) Radar camera fusion via representation learning in autonomous driving. In: Proc. of IEEE CVPR, pp 1672\u20131681","DOI":"10.1109\/CVPRW53098.2021.00183"},{"key":"302_CR38","doi-asserted-by":"crossref","unstructured":"Drews F, Feng D, Faion F et\u00a0al (2022) Deepfusion: A robust and modular 3d object detector for lidars, cameras and radars. In: Proc. of IEEE IROS, pp 560\u2013567","DOI":"10.1109\/IROS47612.2022.9981778"},{"issue":"11","key":"302_CR39","first-page":"22278","volume":"23","author":"Y Du","year":"2021","unstructured":"Du Y, Qin B, Zhao C et al (2021) A novel spatio-temporal synchronization method of roadside asynchronous mmw radar-camera for sensor fusion. IEEE TITS 23(11):22278\u201322289","journal-title":"IEEE TITS"},{"key":"302_CR40","unstructured":"Duan K, Bai S, Xie L et\u00a0al (2023) Centernet++ for object detection. IEEE TPAMI"},{"issue":"4","key":"302_CR41","first-page":"3197","volume":"56","author":"B Erol","year":"2020","unstructured":"Erol B, Gurbuz SZ, Amin MG (2020) Motion classification using kinematically sifted acgan-synthesized radar micro-doppler signatures. IEEE TAES 56(4):3197\u20133213","journal-title":"IEEE TAES"},{"issue":"7","key":"302_CR42","first-page":"7043","volume":"22","author":"M Faizullin","year":"2022","unstructured":"Faizullin M, Kornilova A, Akhmetyanov A et al (2022) Smartdepthsync: open source synchronized video recording system of smartphone rgb and depth camera range image frames with sub-millisecond precision. IEEE JSEN 22(7):7043\u20137052","journal-title":"IEEE JSEN"},{"key":"302_CR43","unstructured":"Fang G, Ma X, Wang X (2024) Structural pruning for diffusion models. Proc of NIPS 36"},{"key":"302_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105358","volume":"154","author":"X Fang","year":"2025","unstructured":"Fang X, Chen J, Wang Y et al (2025) Epfdnet: camouflaged object detection with edge perception in frequency domain. IVC 154:105358","journal-title":"IVC"},{"issue":"3","key":"302_CR45","first-page":"1341","volume":"22","author":"D Feng","year":"2020","unstructured":"Feng D, Haase-Sch\u00fctz C, Rosenbaum L et al (2020) Deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges. IEEE TITS 22(3):1341\u20131360","journal-title":"IEEE TITS"},{"key":"302_CR46","doi-asserted-by":"crossref","unstructured":"Fu D, Li X, Wen L et\u00a0al (2024) Drive like a human: Rethinking autonomous driving with large language models. In: Proc. of IEEE WACV, pp 910\u2013919","DOI":"10.1109\/WACVW60836.2024.00102"},{"key":"302_CR47","doi-asserted-by":"crossref","unstructured":"Gao T, Xia S, Liu M et\u00a0al (2025) Msnet: Multi-scale network for object detection in remote sensing images. PR 158:110983","DOI":"10.1016\/j.patcog.2024.110983"},{"issue":"4","key":"302_CR48","first-page":"5119","volume":"21","author":"X Gao","year":"2020","unstructured":"Gao X, Xing G, Roy S et al (2020) Ramp-cnn: a novel neural network for enhanced automotive radar object recognition. IEEE JSEN 21(4):5119\u20135132","journal-title":"IEEE JSEN"},{"key":"302_CR49","doi-asserted-by":"crossref","unstructured":"Gao X, Luo Y, Alansari A et\u00a0al (2024) Mmw-carry: Enhancing carry object detection through millimeter-wave radar-camera fusion. arXiv:2402.15897","DOI":"10.1109\/JSEN.2024.3378571"},{"key":"302_CR50","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: Proc. of IEEE CVPR, pp 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"302_CR51","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proc. of IEEE ICCV, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"302_CR52","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T et\u00a0al. (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proc. of IEEE CVPR, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"302_CR53","unstructured":"Glenn J, Alex S, Jirka\u00a0Borovec ea (2022a) ultralytics\/yolov5. https:\/\/github.com\/ultralytics\/yolov5"},{"key":"302_CR54","unstructured":"Glenn J, Chaurasia A, Laughing ea (2022b) Yolo by ultralytics. https:\/\/github.com\/ultralytics\/ultralytics"},{"issue":"11","key":"302_CR55","doi-asserted-by":"publisher","first-page":"12561","DOI":"10.1007\/s10462-023-10453-z","volume":"56","author":"E Goceri","year":"2023","unstructured":"Goceri E (2023) Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev 56(11):12561\u201312605","journal-title":"Artif Intell Rev"},{"key":"302_CR56","doi-asserted-by":"crossref","unstructured":"Gray N, Moraes M, Bian J et\u00a0al (2023) Glare: A dataset for traffic sign detection in sun glare. IEEE TITS","DOI":"10.1109\/TITS.2023.3294411"},{"key":"302_CR57","unstructured":"Gu Y, Wang X, Wu JZ et\u00a0al (2024) Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models. Proc of NIPS 36"},{"key":"302_CR58","unstructured":"Gulino C, Fu J, Luo W et\u00a0al (2024) Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. Proc of NIPS 36"},{"key":"302_CR59","doi-asserted-by":"crossref","unstructured":"He H, Cai J, Liu J et\u00a0al (2024) Pruning self-attentions into convolutional layers in single path. IEEE TPAMI","DOI":"10.1109\/TPAMI.2024.3355890"},{"issue":"9","key":"302_CR60","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE TPAMI 37(9):1904\u20131916","journal-title":"IEEE TPAMI"},{"key":"302_CR61","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et\u00a0al (2016) Deep residual learning for image recognition. In: Proc. of IEEE CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"302_CR62","doi-asserted-by":"crossref","unstructured":"He Y, Ma L, Jiang Z et\u00a0al (2021) Vi-eye: Semantic-based 3d point cloud registration for infrastructure-assisted autonomous driving. In: Proc. of ACM MobiCom, pp 573\u2013586","DOI":"10.1145\/3447993.3483276"},{"key":"302_CR63","unstructured":"Huang K, Shi B, Li X et\u00a0al (2022) Multi-modal sensor fusion for auto driving perception: A survey. arXiv:2202.02703"},{"key":"302_CR64","doi-asserted-by":"crossref","unstructured":"Hwang JJ, Kretzschmar H, Manela J et\u00a0al (2022) Cramnet: Camera-radar fusion with ray-constrained cross-attention for robust 3d object detection. In: Proc. of Springer ECCV, pp 388\u2013405","DOI":"10.1007\/978-3-031-19839-7_23"},{"key":"302_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100553","volume":"48","author":"G Iglesias","year":"2023","unstructured":"Iglesias G, Talavera E, D\u00edaz-\u00c1lvarez A (2023) A survey on gans for computer vision: recent research, analysis and taxonomy. COMPUT SCI REV 48:100553","journal-title":"COMPUT SCI REV"},{"key":"302_CR66","unstructured":"Instruments T (2019) Ti iwr1443 single-chip 76-ghz to 81-ghz mmwave sensor evaluation module. https:\/\/www.ti.com\/tool\/IWR1443BOOST"},{"key":"302_CR67","unstructured":"Iovescu C, Rao S (2017) The fundamentals of millimeter wave sensors. TI pp 1\u20138"},{"key":"302_CR68","doi-asserted-by":"crossref","unstructured":"Jha H, Lodhi V, Chakravarty D (2019) Object detection and identification using vision and radar data fusion system for ground-based navigation. In: Proc. of IEEE SPIN, pp 590\u2013593","DOI":"10.1109\/SPIN.2019.8711717"},{"issue":"3","key":"302_CR69","first-page":"3541","volume":"24","author":"Y Ji","year":"2023","unstructured":"Ji Y, Ni L, Zhao C et al (2023) Tripfield: a 3d potential field model and its applications to local path planning of autonomous vehicles. IEEE TITS 24(3):3541\u20133554","journal-title":"IEEE TITS"},{"key":"302_CR70","doi-asserted-by":"crossref","unstructured":"Jiang S, Lin Z, Li Y et\u00a0al (2021) Flexible high-resolution object detection on edge devices with tunable latency. In: Proc. of ACM MobiCom, pp 559\u2013572","DOI":"10.1145\/3447993.3483274"},{"key":"302_CR71","first-page":"1","volume":"72","author":"T Jiang","year":"2022","unstructured":"Jiang T, Zhuang L, An Q et al (2022) T-rodnet: transformer for vehicular millimeter-wave radar object detection. IEEE TIM 72:1\u201312","journal-title":"IEEE TIM"},{"key":"302_CR72","doi-asserted-by":"crossref","unstructured":"Jin S, Wang P, Boufounos P et\u00a0al (2023) Spatial-domain object detection under mimo-fmcw automotive radar interference. In: Proc. of IEEE ICASSP, pp 1\u20135","DOI":"10.1109\/ICASSP49357.2023.10095409"},{"key":"302_CR73","doi-asserted-by":"crossref","unstructured":"Jin Y, Zhu X, Yue Y et\u00a0al (2024) Cr-dino: A novel camera-radar fusion 2d object detection model based on transformer. IEEE JSEN","DOI":"10.1109\/JSEN.2024.3357775"},{"key":"302_CR74","doi-asserted-by":"crossref","unstructured":"Julca-Aguilar F, Taylor J, Bijelic M et\u00a0al (2021) Gated3d: Monocular 3d object detection from temporal illumination cues. In: Proc. of IEEE ICCV, pp 2938\u20132948","DOI":"10.1109\/ICCV48922.2021.00293"},{"key":"302_CR75","doi-asserted-by":"crossref","unstructured":"Kelkar VA, Gotsis DS, Brooks FJ et\u00a0al (2023) Assessing the ability of generative adversarial networks to learn canonical medical image statistics. IEEE TMI","DOI":"10.1109\/TMI.2023.3241454"},{"key":"302_CR76","first-page":"108625","volume":"37","author":"J Kim","year":"2024","unstructured":"Kim J, Seong M, Choi JW (2024) Crt-fusion: camera, radar, temporal fusion using motion information for 3d object detection. Proc of NIPS 37:108625\u2013108648","journal-title":"Proc of NIPS"},{"key":"302_CR77","doi-asserted-by":"crossref","unstructured":"Kim Y, Kim S, Choi JW et\u00a0al (2023a) Craft: Camera-radar 3d object detection with spatio-contextual fusion transformer. In: Proc. of AAAI, pp 1160\u20131168","DOI":"10.1609\/aaai.v37i1.25198"},{"key":"302_CR78","doi-asserted-by":"crossref","unstructured":"Kim Y, Shin J, Kim S et\u00a0al (2023b) Crn: Camera radar net for accurate, robust, efficient 3d perception. In: Proc. of IEEE ICCV, pp 17615\u201317626","DOI":"10.1109\/ICCV51070.2023.01615"},{"key":"302_CR79","doi-asserted-by":"crossref","unstructured":"Koide K, Yokozuka M, Oishi S et\u00a0al (2021) Voxelized gicp for fast and accurate 3d point cloud registration. In: Proc. of IEEE ICRA, pp 11054\u201311059","DOI":"10.1109\/ICRA48506.2021.9560835"},{"issue":"4","key":"302_CR80","first-page":"3638","volume":"24","author":"VR Kumar","year":"2023","unstructured":"Kumar VR, Eising C, Witt C et al (2023) Surround-view fisheye camera perception for automated driving: overview, survey & challenges. IEEE TITS 24(4):3638\u20133659","journal-title":"IEEE TITS"},{"key":"302_CR81","doi-asserted-by":"crossref","unstructured":"Law H, Teng Y, Russakovsky O et\u00a0al (2019) Cornernet-lite: Efficient keypoint based object detection. arXiv:1904.08900","DOI":"10.5244\/C.34.5"},{"issue":"3","key":"302_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3564289","volume":"28","author":"J Lee","year":"2023","unstructured":"Lee J, Wang P, Xu R et al (2023) Virtuoso: energy-and latency-aware streamlining of streaming videos on systems-on-chips. ACM TODAES 28(3):1\u201332","journal-title":"ACM TODAES"},{"key":"302_CR83","unstructured":"Li C, Li L, Jiang H et\u00a0al (2022a) Yolov6: A single-stage object detection framework for industrial applications. arXiv:2209.02976"},{"key":"302_CR84","unstructured":"Li J, Hu Y (2021) Dpointnet: A density-oriented pointnet for 3d object detection in point clouds. arXiv:2102.03747"},{"key":"302_CR85","doi-asserted-by":"crossref","unstructured":"Li J, Li B, Wang L et\u00a0al (2023a) Passive multi-user gait identification through micro-doppler calibration using mmwave radar. IEEE IoTJ","DOI":"10.1109\/JIOT.2023.3312668"},{"key":"302_CR86","unstructured":"Li J, Ji W, Wang S et\u00a0al (2024a) Dvsod: Rgb-d video salient object detection. Proc of NIPS 36"},{"key":"302_CR87","doi-asserted-by":"crossref","unstructured":"Li P, Wang P, Berntorp K et\u00a0al (2022b) Exploiting temporal relations on radar perception for autonomous driving. In: Proc. of IEEE CVPR, pp 17071\u201317080","DOI":"10.1109\/CVPR52688.2022.01656"},{"key":"302_CR88","doi-asserted-by":"crossref","unstructured":"Li P, Wang T, He Z et\u00a0al (2023b) Spatiotemporal weighted micro-doppler spectrum design for soft synchronization fmcw radar. IEEE TIM","DOI":"10.1109\/TIM.2023.3298408"},{"key":"302_CR89","doi-asserted-by":"crossref","unstructured":"Li X, Liu Y, Lakshminarasimhan V et\u00a0al (2023c) Globally optimal robust radar calibration in intelligent transportation systems. IEEE TITS","DOI":"10.1109\/TITS.2023.3251183"},{"key":"302_CR90","doi-asserted-by":"crossref","unstructured":"Li Y, Padmanabhan A, Zhao P et\u00a0al (2020) Reducto: On-camera filtering for resource-efficient real-time video analytics. In: Proc. of ACM SIGCOMM, pp 359\u2013376","DOI":"10.1145\/3387514.3405874"},{"key":"302_CR91","doi-asserted-by":"crossref","unstructured":"Li Y, Yang Y, Lei Z (2025a) Rctrans: Radar-camera transformer via radar densifier and sequential decoder for 3d object detection. In: Proc. of AAAI, pp 5048\u20135056","DOI":"10.1609\/aaai.v39i5.32535"},{"key":"302_CR92","doi-asserted-by":"crossref","unstructured":"Li Z, Ai F, Song Y et\u00a0al (2024b) Pcgnet: Point cloud generation network for 3d perception using monocular images and radar. IEEE TIV","DOI":"10.1109\/TIV.2024.3380166"},{"key":"302_CR93","doi-asserted-by":"crossref","unstructured":"Li Z, Wang Y, Xu D et\u00a0al (2025b) Tbnet: A texture and boundary-aware network for small weak object detection in remote-sensing imagery. PR 158:110976","DOI":"10.1016\/j.patcog.2024.110976"},{"issue":"4","key":"302_CR94","first-page":"941","volume":"15","author":"TY Lim","year":"2021","unstructured":"Lim TY, Markowitz SA, Do MN (2021) Radical: a synchronized fmcw radar, depth, imu and rgb camera data dataset with low-level fmcw radar signals. IEEE JSTSP 15(4):941\u2013953","journal-title":"IEEE JSTSP"},{"key":"302_CR95","doi-asserted-by":"crossref","unstructured":"Lin C, He Z, Qiu Y et\u00a0al (2025) A method of time alignment in bev features for multimodal fusion object detection of intelligent vehicles. IEEE TITS","DOI":"10.1109\/TITS.2025.3557131"},{"key":"302_CR96","doi-asserted-by":"crossref","unstructured":"Lin JT, Dai D, Van\u00a0Gool L (2020) Depth estimation from monocular images and sparse radar data. In: Proc. of IEEE IROS, pp 10233\u201310240","DOI":"10.1109\/IROS45743.2020.9340998"},{"key":"302_CR97","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R et\u00a0al (2017) Feature pyramid networks for object detection. In: Proc. of IEEE CVPR, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"302_CR98","doi-asserted-by":"crossref","unstructured":"Lin Y, Le\u00a0Kernec J (2017) Performance analysis of classification algorithms for activity recognition using micro-doppler feature. In: Proc. of IEEE CIS, pp 480\u2013483","DOI":"10.1109\/CIS.2017.00111"},{"key":"302_CR99","doi-asserted-by":"crossref","unstructured":"Lin Z, Liu Z, Xia Z et\u00a0al (2024) Rcbevdet: Radar-camera fusion in bird\u2019s eye view for 3d object detection. In: Proc. of IEEE CVPR, pp 14928\u201314937","DOI":"10.1109\/CVPR52733.2024.01414"},{"issue":"5","key":"302_CR100","first-page":"106","volume":"40","author":"F Liu","year":"2023","unstructured":"Liu F, Zheng L, Cui Y et al (2023) Seventy years of radar and communications: the road from separation to integration. IEEE SPM 40(5):106\u2013121","journal-title":"IEEE SPM"},{"key":"302_CR101","doi-asserted-by":"crossref","unstructured":"Liu J, Fan X, Huang Z et\u00a0al (2022) Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. In: Proc. of IEEE CVPR, pp 5802\u20135811","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"302_CR102","doi-asserted-by":"crossref","unstructured":"Liu J, Zhao Q, Xiong W et\u00a0al (2023b) Smurf: Spatial multi-representation fusion for 3d object detection with 4d imaging radar. IEEE TIV","DOI":"10.1109\/IV55156.2024.10588505"},{"key":"302_CR103","doi-asserted-by":"crossref","unstructured":"Liu R, Ge Y, Choi CL et\u00a0al (2021) Divco: Diverse conditional image synthesis via contrastive generative adversarial network. In: Proc. of IEEE CVPR, pp 16377\u201316386","DOI":"10.1109\/CVPR46437.2021.01611"},{"key":"302_CR104","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D et\u00a0al (2016) Ssd: Single shot multibox detector. In: Proc. of Springer ECCV, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"302_CR105","unstructured":"Liu Y, Wang F, Wang N et\u00a0al (2024) Echoes beyond points: Unleashing the power of raw radar data in multi-modality fusion. Proc of NIPS 36"},{"key":"302_CR106","doi-asserted-by":"crossref","unstructured":"Lo CC, Vandewalle P (2023) Rcdpt: Radar-camera fusion dense prediction transformer. In: Proc. of IEEE ICASSP, pp 1\u20135","DOI":"10.1109\/ICASSP49357.2023.10096129"},{"key":"302_CR107","doi-asserted-by":"crossref","unstructured":"Lu B, Sun Y, Yang Z et\u00a0al (2024) Hrnet: 3d object detection network for point cloud with hierarchical refinement. PR 149:110254","DOI":"10.1016\/j.patcog.2024.110254"},{"key":"302_CR108","doi-asserted-by":"crossref","unstructured":"Lu S, Zhuo G, Xiong L et\u00a0al (2023a) Efficient deep-learning 4d automotive radar odometry method. IEEE TIV","DOI":"10.1109\/TIV.2023.3311102"},{"key":"302_CR109","doi-asserted-by":"crossref","unstructured":"Lu Y, Xu C, Wei X et\u00a0al (2023b) Open-vocabulary point-cloud object detection without 3d annotation. In: Proc. of IEEE CVPR, pp 1190\u20131199","DOI":"10.1109\/CVPR52729.2023.00121"},{"key":"302_CR110","doi-asserted-by":"crossref","unstructured":"Luo L, Cao SY, Li X et\u00a0al (2025) Bevplace++: Fast, robust, and lightweight lidar global localization for unmanned ground vehicles. IEEE T-RO","DOI":"10.1109\/TRO.2025.3585385"},{"key":"302_CR111","doi-asserted-by":"crossref","unstructured":"Ma X, Wang Z, Li H et\u00a0al (2019) Accurate monocular 3d object detection via color-embedded 3d reconstruction for autonomous driving. In: Proc. of IEEE ICCV, pp 6851\u20136860","DOI":"10.1109\/ICCV.2019.00695"},{"key":"302_CR112","doi-asserted-by":"crossref","unstructured":"Ma X, Ouyang W, Simonelli A et\u00a0al (2023) 3d object detection from images for autonomous driving: a survey. IEEE TPAMI","DOI":"10.1109\/TPAMI.2023.3346386"},{"key":"302_CR113","doi-asserted-by":"crossref","unstructured":"Major B, Fontijne D, Ansari A et\u00a0al (2019) Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors. In: Proc. of IEEE ICCV Workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00121"},{"key":"302_CR114","doi-asserted-by":"crossref","unstructured":"Marathe A, Jain P, Walambe R et\u00a0al (2022) Restorex-ai: A contrastive approach towards guiding image restoration via explainable ai systems. In: Proc. of IEEE CVPR, pp 3030\u20133039","DOI":"10.1109\/CVPRW56347.2022.00342"},{"key":"302_CR115","doi-asserted-by":"crossref","unstructured":"Masih S, Hart J, Singhal P et\u00a0al (2018) Synchronizing multiple data streams by time for vehicle control. In: Proc. of IEEE IV, pp 1686\u20131691","DOI":"10.1109\/IVS.2018.8500470"},{"key":"302_CR116","unstructured":"Matuszka T, Barton I, Butykai \u00c1 et\u00a0al (2022) aimotive dataset: A multimodal dataset for robust autonomous driving with long-range perception. arXiv:2211.09445"},{"key":"302_CR117","doi-asserted-by":"crossref","unstructured":"Meinhardt T, Kirillov A, Leal-Taixe L et\u00a0al (2022) Trackformer: Multi-object tracking with transformers. In: Proc. of IEEE CVPR, pp 8844\u20138854","DOI":"10.1109\/CVPR52688.2022.00864"},{"key":"302_CR118","doi-asserted-by":"crossref","unstructured":"Meng Z, Fu S, Yan J et\u00a0al (2020) Gait recognition for co-existing multiple people using millimeter wave sensing. In: Proc. of AAAI, pp 849\u2013856","DOI":"10.1609\/aaai.v34i01.5430"},{"key":"302_CR119","unstructured":"Meyer M, Kuschk G (2019) Deep learning based 3d object detection for automotive radar and camera. In: Proc. of IEEE EuRAD, pp 133\u2013136"},{"key":"302_CR120","doi-asserted-by":"crossref","unstructured":"Meyer M, Kuschk G, Tomforde S (2021) Graph convolutional networks for 3d object detection on radar data. In: Proc. of IEEE ICCV, pp 3060\u20133069","DOI":"10.1109\/ICCVW54120.2021.00340"},{"key":"302_CR121","doi-asserted-by":"crossref","unstructured":"Mostajabi M, Wang CM, Ranjan D et\u00a0al (2020) High-resolution radar dataset for semi-supervised learning of dynamic objects. In: Proc. of IEEE CVPR Workshops, pp 100\u2013101","DOI":"10.1109\/CVPRW50498.2020.00058"},{"key":"302_CR122","doi-asserted-by":"crossref","unstructured":"Munir M, Avery W, Marculescu R (2023) Mobilevig: Graph-based sparse attention for mobile vision applications. In: Proc. of IEEE CVPR, pp 2210\u20132218","DOI":"10.1109\/CVPRW59228.2023.00215"},{"key":"302_CR123","doi-asserted-by":"crossref","unstructured":"Nabati R, Qi H (2019) Rrpn: Radar region proposal network for object detection in autonomous vehicles. In: Proc. of IEEE ICIP, pp 3093\u20133097","DOI":"10.1109\/ICIP.2019.8803392"},{"key":"302_CR124","unstructured":"Nabati R, Qi H (2020) Radar-camera sensor fusion for joint object detection and distance estimation in autonomous vehicles. arXiv:2009.08428"},{"key":"302_CR125","doi-asserted-by":"crossref","unstructured":"Nabati R, Qi H (2021) Centerfusion: Center-based radar and camera fusion for 3d object detection. In: Proc. of IEEE WACV, pp 1527\u20131536","DOI":"10.1109\/IVWorkshops54471.2021.9669223"},{"key":"302_CR126","doi-asserted-by":"crossref","unstructured":"Niu Y, Zhou S, Dong Y et\u00a0al (2024) Bidirectional feature learning network for rgb-d salient object detection. PR 150:110304","DOI":"10.1016\/j.patcog.2024.110304"},{"key":"302_CR127","doi-asserted-by":"crossref","unstructured":"Ouaknine A, Newson A, Rebut J et\u00a0al (2021) Carrada dataset: Camera and automotive radar with range-angle-doppler annotations. In: Proc. of IEEE ICPR, pp 5068\u20135075","DOI":"10.1109\/ICPR48806.2021.9413181"},{"key":"302_CR128","first-page":"3819","volume":"35","author":"DH Paek","year":"2022","unstructured":"Paek DH, Kong SH, Wijaya KT (2022) K-radar: 4d radar object detection for autonomous driving in various weather conditions. Proc of NIPS 35:3819\u20133829","journal-title":"Proc of NIPS"},{"issue":"2","key":"302_CR129","first-page":"4961","volume":"7","author":"A Palffy","year":"2022","unstructured":"Palffy A, Pool E, Baratam S et al (2022) Multi-class road user detection with 3+ 1d radar in the view-of-delft dataset. IEEE RAL 7(2):4961\u20134968","journal-title":"IEEE RAL"},{"key":"302_CR130","doi-asserted-by":"crossref","unstructured":"Pan J, Bulat A, Tan F et\u00a0al (2022) Edgevits: Competing light-weight cnns on mobile devices with vision transformers. In: Proc. of Springer ECCV, pp 294\u2013311","DOI":"10.1007\/978-3-031-20083-0_18"},{"key":"302_CR131","doi-asserted-by":"crossref","unstructured":"Pan Z, Ding F, Zhong H et\u00a0al (2023) Moving object detection and tracking with 4d radar point cloud. arXiv:2309.09737","DOI":"10.1109\/ICRA57147.2024.10610368"},{"key":"302_CR132","doi-asserted-by":"crossref","unstructured":"Pang S, Morris D, Radha H (2023) Transcar: Transformer-based camera-and-radar fusion for 3d object detection. In: Proc. of IEEE IROS, pp 10902\u201310909","DOI":"10.1109\/IROS55552.2023.10341793"},{"key":"302_CR133","doi-asserted-by":"crossref","unstructured":"Park JK, Park JH, Kim KT (2023) Multipath signal mitigation for indoor localization based on mimo fmcw radar system. IEEE IoTJ","DOI":"10.1109\/JIOT.2023.3292349"},{"key":"302_CR134","doi-asserted-by":"crossref","unstructured":"Peri N, Li M, Wilson B et\u00a0al (2023) An empirical analysis of range for 3d object detection. In: Proc. of IEEE CVPR, pp 4074\u20134083","DOI":"10.1109\/ICCVW60793.2023.00440"},{"issue":"5","key":"302_CR135","first-page":"1401","volume":"37","author":"J Per\u0161i\u0107","year":"2021","unstructured":"Per\u0161i\u0107 J, Petrovi\u0107 L, Markovi\u0107 I et al (2021) Spatiotemporal multisensor calibration via gaussian processes moving target tracking. IEEE TRO 37(5):1401\u20131415","journal-title":"IEEE TRO"},{"key":"302_CR136","unstructured":"Qi CR, Su H, Mo K et\u00a0al (2017a) Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proc. of IEEE CVPR, pp 652\u2013660"},{"key":"302_CR137","unstructured":"Qi CR, Yi L, Su H et\u00a0al (2017b) Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Proc of NIPS 30"},{"key":"302_CR138","doi-asserted-by":"crossref","unstructured":"Qian K, Zhu S, Zhang X et\u00a0al (2021) Robust multimodal vehicle detection in foggy weather using complementary lidar and radar signals. In: Proc. of IEEE CVPR, pp 444\u2013453","DOI":"10.1109\/CVPR46437.2021.00051"},{"key":"302_CR139","doi-asserted-by":"crossref","unstructured":"Rao S (2017) Introduction to mmwave sensing: Fmcw radars. TI mmWave Training Series pp 1\u201311","DOI":"10.1016\/B978-0-12-804418-6.00001-7"},{"issue":"5","key":"302_CR140","first-page":"5668","volume":"21","author":"R Ravindran","year":"2020","unstructured":"Ravindran R, Santora MJ, Jamali MM (2020) Multi-object detection and tracking, based on dnn, for autonomous vehicles: a review. IEEE JSEN 21(5):5668\u20135677","journal-title":"IEEE JSEN"},{"key":"302_CR141","doi-asserted-by":"crossref","unstructured":"Rebut J, Ouaknine A, Malik W et\u00a0al (2022) Raw high-definition radar for multi-task learning. In: Proc. of IEEE CVPR, pp 17021\u201317030","DOI":"10.1109\/CVPR52688.2022.01651"},{"key":"302_CR142","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767"},{"key":"302_CR143","unstructured":"Ren S, He K, Girshick R et\u00a0al (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Proc of NIPS 28"},{"key":"302_CR144","doi-asserted-by":"crossref","unstructured":"Schumann O, Hahn M, Scheiner N et\u00a0al (2021) Radarscenes: A real-world radar point cloud data set for automotive applications. In: Proc. of IEEE FUSION, pp 1\u20138","DOI":"10.23919\/FUSION49465.2021.9627037"},{"key":"302_CR145","doi-asserted-by":"crossref","unstructured":"Seeliger F, Dietmayer K (2014) Inter-vehicle information-fusion with shared perception information. In: Proc. of IEEE ITSC, pp 2087\u20132093","DOI":"10.1109\/ITSC.2014.6958011"},{"key":"302_CR146","unstructured":"Shah M, Huang Z, Laddha A et\u00a0al (2020) Liranet: End-to-end trajectory prediction using spatio-temporal radar fusion. arXiv:2010.00731"},{"key":"302_CR147","doi-asserted-by":"crossref","unstructured":"Sheeny M, De\u00a0Pellegrin E, Mukherjee S et\u00a0al (2021) Radiate: A radar dataset for automotive perception in bad weather. In: Proc. of IEEE ICRA, pp 1\u20137","DOI":"10.1109\/ICRA48506.2021.9562089"},{"key":"302_CR148","doi-asserted-by":"crossref","unstructured":"Shi G, Wang K, Li R et\u00a0al (2023a) Real-time point cloud object detection via voxel-point geometry abstraction. IEEE TITS","DOI":"10.1109\/TITS.2023.3259582"},{"key":"302_CR149","doi-asserted-by":"crossref","unstructured":"Shi S, Cui J, Jiang Z et\u00a0al (2022) Vips: Real-time perception fusion for infrastructure-assisted autonomous driving. In: Proc. of ACM MobiCom, pp 133\u2013146","DOI":"10.1145\/3495243.3560539"},{"key":"302_CR150","doi-asserted-by":"crossref","unstructured":"Shi Y, Wang N, Guo X (2023b) Yolov: Making still image object detectors great at video object detection. In: Proc. of AAAI, pp 2254\u20132262","DOI":"10.1609\/aaai.v37i2.25320"},{"key":"302_CR151","doi-asserted-by":"crossref","unstructured":"Shuai X, Shen Y, Tang Y et\u00a0al (2021) millieye: A lightweight mmwave radar and camera fusion system for robust object detection. In: Proc. of ACM IoTDI, pp 145\u2013157","DOI":"10.1145\/3450268.3453532"},{"issue":"3","key":"302_CR152","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1109\/TPAMI.2020.3025077","volume":"44","author":"A Simonelli","year":"2020","unstructured":"Simonelli A, Bulo SR, Porzi L et al (2020) Disentangling monocular 3d object detection: from single to multi-class recognition. IEEE TPAMI 44(3):1219\u20131231","journal-title":"IEEE TPAMI"},{"key":"302_CR153","doi-asserted-by":"crossref","unstructured":"Singh A (2023) Vision-radar fusion for robotics bev detections: A survey. In: Proc. of IEEE IV, pp 1\u20137","DOI":"10.1109\/IV55152.2023.10186647"},{"key":"302_CR154","doi-asserted-by":"crossref","unstructured":"Song Z, Wei H, Jia C et\u00a0al (2023) Vp-net: Voxels as points for 3d object detection. IEEE TGRS","DOI":"10.1109\/TGRS.2023.3271020"},{"key":"302_CR155","doi-asserted-by":"crossref","unstructured":"St\u00e4cker L, Heidenreich P, Rambach J et\u00a0al (2022) Fusion point pruning for optimized 2d object detection with radar-camera fusion. In: Proc. of IEEE WACV, pp 3087\u20133094","DOI":"10.1109\/WACV51458.2022.00134"},{"key":"302_CR156","doi-asserted-by":"crossref","unstructured":"Stove AG (1992) Linear fmcw radar techniques. In: IEE Proceedings F (Radar and Signal Processing), pp 343\u2013350","DOI":"10.1049\/ip-f-2.1992.0048"},{"issue":"4","key":"302_CR157","first-page":"98","volume":"37","author":"S Sun","year":"2020","unstructured":"Sun S, Petropulu AP, Poor HV (2020) Mimo radar for advanced driver-assistance systems and autonomous driving: advantages and challenges. IEEE SPM 37(4):98\u2013117","journal-title":"IEEE SPM"},{"key":"302_CR158","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2023.109165","volume":"213","author":"Q Tang","year":"2023","unstructured":"Tang Q, Liang J, Zhu F (2023) A comparative review on multi-modal sensors fusion based on deep learning. Signal Process 213:109165","journal-title":"Signal Process"},{"key":"302_CR159","doi-asserted-by":"crossref","unstructured":"Tian H, Chen Y, Dai J et\u00a0al (2021) Unsupervised object detection with lidar clues. In: Proc. of IEEE CVPR, pp 5962\u20135972","DOI":"10.1109\/CVPR46437.2021.00590"},{"key":"302_CR160","first-page":"1882","volume":"30","author":"NT Tran","year":"2021","unstructured":"Tran NT, Tran VH, Nguyen NB et al (2021) On data augmentation for gan training. IEEE TIP 30:1882\u20131897","journal-title":"IEEE TIP"},{"key":"302_CR161","doi-asserted-by":"crossref","unstructured":"Tu Z, Ma Y, Li Z et\u00a0al (2022) Rgbt salient object detection: A large-scale dataset and benchmark. IEEE TMM","DOI":"10.1109\/TMM.2022.3171688"},{"key":"302_CR162","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023a) Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proc. of IEEE CVPR, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"2","key":"302_CR163","first-page":"1699","volume":"8","author":"K Wang","year":"2022","unstructured":"Wang K, Zhou T, Li X et al (2022) Performance and challenges of 3d object detection methods in complex scenes for autonomous driving. IEEE TITS 8(2):1699\u20131716","journal-title":"IEEE TITS"},{"key":"302_CR164","doi-asserted-by":"crossref","unstructured":"Wang L, Zhang Z, Di X et\u00a0al (2021a) A roadside camera-radar sensing fusion system for intelligent transportation. In: Proc. of IEEE EuRAD, pp 282\u2013285","DOI":"10.1109\/EuRAD48048.2021.00079"},{"key":"302_CR165","doi-asserted-by":"crossref","unstructured":"Wang L, Zhang X, Song Z et\u00a0al (2023b) Multi-modal 3d object detection in autonomous driving: A survey and taxonomy. IEEE TIV","DOI":"10.1109\/TIV.2023.3264658"},{"key":"302_CR166","doi-asserted-by":"crossref","unstructured":"Wang N, Gao Y, Chen H et\u00a0al (2020) Nas-fcos: Fast neural architecture search for object detection. In: Proc. of IEEE CVPR, pp 11943\u201311951","DOI":"10.1109\/CVPR42600.2020.01196"},{"key":"302_CR167","doi-asserted-by":"crossref","unstructured":"Wang W, Wang Y, Zhang J et\u00a0al (2025) Multiple attention mechanism for camera-radar fusion object detection. IEEE TVT","DOI":"10.1109\/TVT.2025.3531379"},{"key":"302_CR168","doi-asserted-by":"crossref","unstructured":"Wang Y, Jiang Z, Gao X et\u00a0al (2021b) Rodnet: Radar object detection using cross-modal supervision. In: Proc. of IEEE CVPR, pp 504\u2013513","DOI":"10.1109\/WACV48630.2021.00055"},{"key":"302_CR169","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang G, Hsu HM et\u00a0al (2021c) Rethinking of radar\u2019s role: A camera-radar dataset and systematic annotator via coordinate alignment. In: Proc. of IEEE CVPR, pp 2815\u20132824","DOI":"10.1109\/CVPRW53098.2021.00316"},{"key":"302_CR170","doi-asserted-by":"crossref","unstructured":"Wang Y, Du H, Cheng Z et\u00a0al (2023c) Krrnet: Keypoint relational regression network for bottom-up anchor-free object detection. IEEE TCSVT","DOI":"10.1109\/TCSVT.2023.3305289"},{"key":"302_CR171","unstructured":"Wang Y, Deng J, Hou Y et\u00a0al (2024) Club: Cluster meets bev for lidar-based 3d object detection. Proc of NIPS 36"},{"key":"302_CR172","first-page":"1","volume":"72","author":"T Watanabe","year":"2023","unstructured":"Watanabe T, Akamine Y (2023) Low-cost radar cross section measurement with a resin-made model coated with conductive paste. IEEE TIM 72:1\u201310","journal-title":"IEEE TIM"},{"key":"302_CR173","doi-asserted-by":"crossref","unstructured":"Wei Q, Jiang X, Liu Y et\u00a0al (2024) Small object detection on the water surface based on radar and camera fusion. In: Proc. of IEEE ICASSP, pp 8381\u20138385","DOI":"10.1109\/ICASSP48485.2024.10446880"},{"key":"302_CR174","doi-asserted-by":"crossref","unstructured":"Wen Z, Xu H, Liu C et\u00a0al (2023) Occlubev: Occlusion aware spatiotemporal modeling for multi-view 3d object detection. In: Proc. of ACM MM, pp 4074\u20134083","DOI":"10.1145\/3581783.3613798"},{"key":"302_CR175","unstructured":"Weng X, Man Y, Cheng D et\u00a0al (2020) All-in-one drive: A large-scale comprehensive perception dataset with high-density long-range point clouds. arxiv"},{"key":"302_CR176","unstructured":"WHO (2023) Global status report on road safety 2023. World Health Organization"},{"key":"302_CR177","doi-asserted-by":"crossref","unstructured":"Wise E, Per\u0161i\u0107 J, Grebe C et\u00a0al (2021) A continuous-time approach for 3d radar-to-camera extrinsic calibration. In: Proc. of IEEE ICRA, pp 13164\u201313170","DOI":"10.1109\/ICRA48506.2021.9561938"},{"key":"302_CR178","doi-asserted-by":"crossref","unstructured":"Wise E, Cheng Q, Kelly J (2023) Spatiotemporal calibration of 3-d millimetre-wavelength radar-camera pairs. IEEE TRO","DOI":"10.1109\/TRO.2023.3311680"},{"key":"302_CR179","doi-asserted-by":"crossref","unstructured":"Wu H, Wen C, Li W et\u00a0al (2023a) Transformation-equivariant 3d object detection for autonomous driving. In: Proc. of AAAI, pp 2795\u20132802","DOI":"10.1609\/aaai.v37i3.25380"},{"key":"302_CR180","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102647","volume":"113","author":"Y Wu","year":"2025","unstructured":"Wu Y (2025) Fusion-based modeling of an intelligent algorithm for enhanced object detection using a deep learning approach on radar and camera data. Information Fusion 113:102647","journal-title":"Information Fusion"},{"key":"302_CR181","doi-asserted-by":"crossref","unstructured":"Wu Z, Chen G, Gan Y et\u00a0al (2023b) Mvfusion: Multi-view 3d object detection with semantic-aligned radar and camera fusion. In: Proc. of IEEE ICRA, pp 2766\u20132773","DOI":"10.1109\/ICRA48891.2023.10161329"},{"key":"302_CR182","doi-asserted-by":"crossref","unstructured":"Wu Z, Wu Y, Wang X et\u00a0al (2024) A robust diffusion modeling framework for radar camera 3d object detection. In: Proc. of IEEE CVPR, pp 3282\u20133292","DOI":"10.1109\/WACV57701.2024.00325"},{"key":"302_CR183","doi-asserted-by":"crossref","unstructured":"Xie G, Chen Z, Gao M et\u00a0al (2024a) Ppf-det: Point-pixel fusion for multi-modal 3d object detection. IEEE TITS","DOI":"10.1109\/TITS.2023.3347078"},{"key":"302_CR184","doi-asserted-by":"crossref","unstructured":"Xie Q, Lai YK, Wu J et\u00a0al (2020) Mlcvnet: Multi-level context votenet for 3d object detection. In: Proc. of IEEE CVPR, pp 10447\u201310456","DOI":"10.1109\/CVPR42600.2020.01046"},{"key":"302_CR185","doi-asserted-by":"crossref","unstructured":"Xie X, Wu D, Xie M et\u00a0al (2024b) Ghostformer: Efficiently amalgamated cnn-transformer architecture for object detection. PR 148:110172","DOI":"10.1016\/j.patcog.2023.110172"},{"key":"302_CR186","doi-asserted-by":"crossref","unstructured":"Xiong K, Gong S, Ye X et\u00a0al (2023a) Cape: Camera view position embedding for multi-view 3d object detection. In: Proc. of IEEE CVPR, pp 21570\u201321579","DOI":"10.1109\/CVPR52729.2023.02066"},{"key":"302_CR187","doi-asserted-by":"crossref","unstructured":"Xiong W, Liu J, Huang T et\u00a0al (2023b) Lxl: Lidar exclusive lean 3d object detection with 4d imaging radar and camera fusion. arXiv:2307.00724","DOI":"10.1109\/IV55156.2024.10588781"},{"key":"302_CR188","doi-asserted-by":"crossref","unstructured":"Xu D, Zhou A, Wang G et\u00a0al (2022a) Tutti: coupling 5g ran and mobile edge computing for latency-critical video analytics. In: Proc. of ACM MobiCom, pp 729\u2013742","DOI":"10.1145\/3495243.3560538"},{"key":"302_CR189","unstructured":"Xu D, Yin W, Jin X et\u00a0al (2023a) Llmcad: Fast and scalable on-device large language model inference. arXiv:2309.04255"},{"key":"302_CR190","doi-asserted-by":"crossref","unstructured":"Xu J, Peng L, Cheng H et\u00a0al (2023b) Mononerd: Nerf-like representations for monocular 3d object detection. In: Proc. of IEEE ICCV, pp 6814\u20136824","DOI":"10.1109\/ICCV51070.2023.00627"},{"key":"302_CR191","doi-asserted-by":"crossref","unstructured":"Xu R, Mu F, Lee J et\u00a0al (2022b) Smartadapt: Multi-branch object detection framework for videos on mobiles. In: Proc. of IEEE CVPR, pp 2528\u20132538","DOI":"10.1109\/CVPR52688.2022.00256"},{"key":"302_CR192","doi-asserted-by":"crossref","unstructured":"Xu R, Xiang Z, Zhang C et\u00a0al (2025) Sckd: Semi-supervised cross-modality knowledge distillation for 4d radar object detection. In: Proc. of AAAI, pp 8933\u20138941","DOI":"10.1609\/aaai.v39i9.32966"},{"key":"302_CR193","doi-asserted-by":"crossref","unstructured":"Xu X, Liang W, Zhao J et\u00a0al (2021) Tiny fcos: A lightweight anchor-free object detection algorithm for mobile scenarios. MOBILE NETW APPL pp 1\u201311","DOI":"10.1007\/s11036-021-01845-y"},{"key":"302_CR194","doi-asserted-by":"crossref","unstructured":"Yang B, Guo R, Liang M et\u00a0al (2020a) Radarnet: Exploiting radar for robust perception of dynamic objects. In: Proc. of Springer ECCV, pp 496\u2013512","DOI":"10.1007\/978-3-030-58523-5_29"},{"key":"302_CR195","doi-asserted-by":"crossref","unstructured":"Yang L, Tang T, Li J et\u00a0al (2025) Bevheight++: Toward robust visual centric 3d object detection. IEEE TPAML","DOI":"10.1109\/TPAMI.2025.3549711"},{"key":"302_CR196","doi-asserted-by":"crossref","unstructured":"Yang X, Liu J, Chen Y et\u00a0al (2020b) Mu-id: Multi-user identification through gaits using millimeter wave radios. In: Proc. of IEEE INFOCOM, pp 2589\u20132598","DOI":"10.1109\/INFOCOM41043.2020.9155471"},{"key":"302_CR197","doi-asserted-by":"crossref","unstructured":"Yang Z, Liu S, Hu H et\u00a0al (2019) Reppoints: Point set representation for object detection. In: Proc. of IEEE ICCV, pp 9657\u20139666","DOI":"10.1109\/ICCV.2019.00975"},{"key":"302_CR198","doi-asserted-by":"crossref","unstructured":"Yao G, Xuan Y, Li X et\u00a0al (2024) Cmr-agent: Learning a cross-modal agent for iterative image-to-point cloud registration. In: Proc. of IEEE IROS, pp 13458\u201313465","DOI":"10.1109\/IROS58592.2024.10802594"},{"key":"302_CR199","unstructured":"Yao S, Guan R, Huang X et\u00a0al (2023) Radar-camera fusion for object detection and semantic segmentation in autonomous driving: A comprehensive review. IEEE TIV"},{"key":"302_CR200","doi-asserted-by":"crossref","unstructured":"Yu Z, Wan W, Ren M et\u00a0al (2023) Sparsefusion3d: Sparse sensor fusion for 3d object detection by radar and camera in environmental perception. IEEE TIV","DOI":"10.1109\/TIV.2023.3331972"},{"key":"302_CR201","unstructured":"Yuan X, Zheng Z, Li Y et\u00a0al (2025) Strip r-cnn: Large strip convolution for remote sensing object detection. arXiv:2501.03775"},{"key":"302_CR202","unstructured":"Yuan Y, Fu R, Huang L et\u00a0al (2021) Hrformer: High-resolution transformer for dense prediction. arXiv:2110.09408"},{"key":"302_CR203","doi-asserted-by":"crossref","unstructured":"Yuance C, Ding H, Han D et\u00a0al (2023) mmyodar: Lightweight and robust object detection using mmwave signals. In: Proc. of IEEE SECON, pp 151\u2013159","DOI":"10.1109\/SECON58729.2023.10287427"},{"key":"302_CR204","doi-asserted-by":"crossref","unstructured":"Zand M, Etemad A, Greenspan M (2022) Objectbox: From centers to boxes for anchor-free object detection. In: Proc. of Springer ECCV, pp 390\u2013406","DOI":"10.1007\/978-3-031-20080-9_23"},{"key":"302_CR205","doi-asserted-by":"crossref","unstructured":"Zhang A, Nowruzi FE, Laganiere R (2021a) Raddet: Range-azimuth-doppler based radar object detection for dynamic road users. In: Proc. of IEEE CRV, pp 95\u2013102","DOI":"10.1109\/CRV52889.2021.00021"},{"key":"302_CR206","doi-asserted-by":"crossref","unstructured":"Zhang G, Li H, Wenger F (2020) Object detection and 3d estimation via an fmcw radar using a fully convolutional network. In: Proc. of IEEE ICASSP, pp 4487\u20134491","DOI":"10.1109\/ICASSP40776.2020.9054511"},{"key":"302_CR207","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang Y, Dayoub F et\u00a0al (2021b) Varifocalnet: An iou-aware dense object detector. In: Proc. of IEEE CVPR, pp 8514\u20138523","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"302_CR208","doi-asserted-by":"crossref","unstructured":"Zhang J, Huang J, Luo Z et\u00a0al (2023a) Da-detr: Domain adaptive detection transformer with information fusion. In: Proc. of IEEE CVPR, pp 23787\u201323798","DOI":"10.1109\/CVPR52729.2023.02278"},{"key":"302_CR209","doi-asserted-by":"crossref","unstructured":"Zhang J, Xi R, He Y et\u00a0al (2023b) A survey of mmwave-based human sensing: Technology, platforms and applications. IEEE COMST","DOI":"10.1109\/COMST.2023.3298300"},{"key":"302_CR210","doi-asserted-by":"crossref","unstructured":"Zhang W, He Z, Liu L et\u00a0al (2021c) Elf: accelerate high-resolution mobile deep vision with content-aware parallel offloading. In: Proc. of ACM MobiCom, pp 201\u2013214","DOI":"10.1145\/3447993.3448628"},{"key":"302_CR211","doi-asserted-by":"crossref","unstructured":"Zhang X, Li Z, Zhang J (2022) Synthesized millimeter-waves for human motion sensing. In: Proc. of ACM SenSys, pp 377\u2013390","DOI":"10.1145\/3560905.3568542"},{"key":"302_CR212","doi-asserted-by":"crossref","unstructured":"Zhang Z, Zhao Y, Li H et\u00a0al (2024) Dvfo: Learning-based dvfs for energy-efficient edge-cloud collaborative inference. IEEE TMC","DOI":"10.1109\/TMC.2024.3357218"},{"key":"302_CR213","doi-asserted-by":"crossref","unstructured":"Zhao L, Song J, Skinner KA (2024) Crkd: Enhanced camera-radar object detection with cross-modality knowledge distillation. arXiv:2403.19104","DOI":"10.1109\/CVPR52733.2024.01465"},{"key":"302_CR214","doi-asserted-by":"crossref","unstructured":"Zheng L, Ma Z, Zhu X et\u00a0al (2022) Tj4dradset: A 4d radar dataset for autonomous driving. In: Proc. of IEEE ITSC, pp 493\u2013498","DOI":"10.1109\/ITSC55140.2022.9922539"},{"key":"302_CR215","doi-asserted-by":"crossref","unstructured":"Zhou D, Fang J, Song X et\u00a0al (2020) Joint 3d instance segmentation and object detection for autonomous driving. In: Proc. of IEEE CVPR, pp 1839\u20131849","DOI":"10.1109\/CVPR42600.2020.00191"},{"key":"302_CR216","doi-asserted-by":"crossref","unstructured":"Zhou QY, Park J, Koltun V (2016) Fast global registration. In: Proc. of Springer ECCV, pp 766\u2013782","DOI":"10.1007\/978-3-319-46475-6_47"},{"issue":"2","key":"302_CR217","first-page":"1523","volume":"8","author":"T Zhou","year":"2023","unstructured":"Zhou T, Chen J, Shi Y et al (2023) Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection. IEEE TIV 8(2):1523\u20131535","journal-title":"IEEE TIV"},{"key":"302_CR218","unstructured":"Zhou X, Wang D, Kr\u00e4henb\u00fchl P (2019) Objects as points. arXiv:1904.07850"},{"key":"302_CR219","doi-asserted-by":"crossref","unstructured":"Zhou Z, Wang P, Liang Z et\u00a0al (2025) Cross-modal 3d representation with multi-view images and point clouds. In: Proc. of IEEE CVPR, pp 3728\u20133739","DOI":"10.1109\/CVPR52734.2025.00353"},{"key":"302_CR220","doi-asserted-by":"crossref","unstructured":"Zhu Z, Zhang Y, Chen H et\u00a0al (2023) Understanding the robustness of 3d object detection with bird\u2019s-eye-view representations in autonomous driving. In: Proc. of IEEE CVPR, pp 21600\u201321610","DOI":"10.1109\/CVPR52729.2023.02069"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00302-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00302-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00302-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:48:21Z","timestamp":1767638901000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00302-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":220,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["302"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00302-0","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]},"assertion":[{"value":"15 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"328"}}