{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T15:39:36Z","timestamp":1785253176710,"version":"3.55.0"},"reference-count":407,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100010665","name":"European Union\u2019s Horizon 2020 Research and Innovation Programme, under Marie Sk\u0142odowska Curie Grant","doi-asserted-by":"publisher","award":["956090"],"award-info":[{"award-number":["956090"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"SPATIAL Project","doi-asserted-by":"publisher","award":["101021808"],"award-info":[{"award-number":["101021808"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/comst.2023.3302474","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T17:40:43Z","timestamp":1691602843000},"page":"2714-2754","source":"Crossref","is-referenced-by-count":101,"title":["A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3412-7603","authenticated-orcid":false,"given":"Dewant","family":"Katare","sequence":"first","affiliation":[{"name":"Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Perino","sequence":"additional","affiliation":[{"name":"elef&#x00F3;nica R&#x0026;D, Telef&#x00F3;nica, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-4606","authenticated-orcid":false,"given":"Jari","family":"Nurmi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4682-6882","authenticated-orcid":false,"given":"Martijn","family":"Warnier","sequence":"additional","affiliation":[{"name":"Department of Multi-Actor Systems, Delft University of Technology, Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6211-8790","authenticated-orcid":false,"given":"Marijn","family":"Janssen","sequence":"additional","affiliation":[{"name":"Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-031X","authenticated-orcid":false,"given":"Aaron Yi","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2865173"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2021.3126650"},{"key":"ref3","author":"Ackerman","year":"2021","journal-title":"What Full Autonomy Means for the Waymo Driver"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920961451"},{"key":"ref5","first-page":"3180","article-title":"Net-Trim: Convex pruning of deep neural networks with performance guarantee","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Aghasi"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00938"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT48636.2020.9061543"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995703"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3417326"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01846"},{"key":"ref12","first-page":"1709","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Alistarh"},{"key":"ref13","article-title":"The convergence of sparsified gradient methods","author":"Alistarh","year":"2018","journal-title":"arXiv:1809.10505"},{"key":"ref14","first-page":"856","article-title":"Compression-aware training of deep networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Alvarez"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2981904"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3052681"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59897-6_9"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304570"},{"key":"ref19","volume-title":"Apolloauto\/Apollo: An Open Autonomous Driving Platform","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3028424"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/BIGCOMP.2017.7881725"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MCOMSTD.001.2000069"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2018.xiv.016"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00112"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00111"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2902850"},{"key":"ref27","first-page":"560","article-title":"SignSGD: Compressed optimisation for non-convex problems","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bernstein"},{"key":"ref28","article-title":"MixMatch: A holistic approach to semi-supervised learning","author":"Berthelot","year":"2019","journal-title":"arXiv:1905.02249"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00337"},{"key":"ref30","volume-title":"Comma.ai","author":"Biasini","year":"2016"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00271"},{"key":"ref32","article-title":"End to end learning for self-driving cars","author":"Bojarski","year":"2016","journal-title":"arXiv:1604.07316"},{"key":"ref33","first-page":"1","article-title":"Towards federated learning at scale: System design","volume-title":"Proc. Mach. Learn. Syst.","author":"Bonawitz"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2897684"},{"key":"ref35","volume-title":"CARMA: Improving Traffic Flows and Safety at Active Work Zones: Public Roads","volume":"85","author":"Bujanovi\u0107","year":"2021"},{"key":"ref36","volume-title":"Autonomous car\u2019s big problem","author":"Buzatu","year":"2021"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304681"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139835"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-020-00896-5"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2024789118"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3318216.3363300"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/MCOMSTD.2017.1700015"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00615"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2884200"},{"key":"ref49","article-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"Choi","year":"2018","journal-title":"arXiv:1805.06085"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00363"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2791533"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2020.102392"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1604.01685"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2965415"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207281"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16207"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3100848"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2906509"},{"key":"ref59","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Denton"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460653"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9565047"},{"key":"ref62","article-title":"SemiFL: Communication efficient semi-supervised federated learning with unlabeled clients","author":"Diao","year":"2021","journal-title":"arXiv:2106.01432"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.3035770"},{"key":"ref64","first-page":"1","article-title":"CARLA: An open urban driving simulator","volume-title":"Proc. Conf. Robot Learn.","author":"Dosovitskiy"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2018.12.009"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989161"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/OJCAS.2022.3174632"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2004.07320"},{"key":"ref70","article-title":"LiDAR-CS dataset: LiDAR point cloud dataset with cross-sensors for 3D object detection","author":"Fang","year":"2023","journal-title":"arXiv:2301.12515"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CYBER.2017.8446162"},{"key":"ref72","article-title":"AUSN: Approximately uniform quantization by adaptively superimposing non-uniform distribution for deep neural networks","author":"Fangxin","year":"2020","journal-title":"arXiv:2007.03903"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2714704"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.01.133"},{"key":"ref75","article-title":"MUAD: Multiple uncertainties for autonomous driving, a benchmark for multiple uncertainty types and tasks","author":"Franchi","year":"2022","journal-title":"arXiv:2203.01437"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.3390\/s21082659"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2019.07.007"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00301"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.7b04576"},{"key":"ref80","article-title":"A2D2: Audi autonomous driving dataset","author":"Geyer","year":"2020","journal-title":"arXiv:2004.06320"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1115\/IMECE2020-24008"},{"key":"ref82","volume-title":"Google Sibling Waymo Launches Fully Autonomous Ride-Hailing Service","author":"Gibbs","year":"2017"},{"key":"ref83","article-title":"Exploring neural networks quantization via layer-wise quantization analysis","author":"Gluska","year":"2020","journal-title":"arXiv:2012.08420"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730187"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1080\/00423114.2015.1037774"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8594394"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2006.889486"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/ASP-DAC47756.2020.9045176"},{"key":"ref90","article-title":"Network decoupling: From regular to depthwise separable convolutions","author":"Guo","year":"2018","journal-title":"arXiv:1808.05517"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58589-1_40"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8206396"},{"key":"ref93","article-title":"SODA10M: A large-scale 2D self\/semi-supervised object detection dataset for autonomous driving","author":"Han","year":"2021","journal-title":"arXiv:2106.11118"},{"key":"ref94","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"Han","year":"2015","journal-title":"arXiv:1510.00149"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01189"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000541"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018409"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2933477"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/309"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.3390\/s18041229"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340757"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8968020"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814110"},{"key":"ref106","article-title":"One thousand and one hours: Self-driving motion prediction dataset","author":"Houston","year":"2020","journal-title":"arXiv:2006.14480"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1007\/s41650-017-0022-x"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC45102.2020.9294515"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2820679"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103189"},{"key":"ref111","article-title":"A game-theoretic framework for autonomous vehicles velocity control: Bridging microscopic differential games and macroscopic mean field games","author":"Huang","year":"2019","journal-title":"arXiv:1903.06053"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1145\/3431920.3439295"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2926463"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2945338"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1602.07360"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC51323.2021.9498627"},{"key":"ref117","article-title":"Learning-based low-rank approximations","author":"Indyk","year":"2019","journal-title":"arXiv:1910.13984"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.9734\/ajrcos\/2021\/v10i230238"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.88"},{"key":"ref120","article-title":"Brain4cars: Car that knows before you do via sensory-fusion deep learning architecture","author":"Jain","year":"2016","journal-title":"arXiv:1601.00740"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00099"},{"key":"ref122","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data","author":"Jeong","year":"2018","journal-title":"arXiv:1811.11479"},{"key":"ref123","article-title":"Federated semi-supervised learning with inter-client consistency & disjoint learning","author":"Jeong","year":"2020","journal-title":"arXiv:2006.12097"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/ivs.2018.8500599"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.01.004"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682464"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2018.00058"},{"key":"ref128","article-title":"KDLSQ-BERT: A quantized bert combining knowledge distillation with learned step size quantization","author":"Jin","year":"2021","journal-title":"arXiv:2101.05938"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00448"},{"key":"ref130","first-page":"35","article-title":"Multispectral object detection for autonomous vehicles","volume-title":"Proc. Thematic Workshops ACM Multimedia","author":"Karasawa"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/CISS56502.2023.10089724"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/ISSPIT.2018.8705101"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/ICVES.2019.8906442"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/NAECON46414.2019.9057988"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/SEC54971.2022.00050"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPS.2018.00035"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-34208-5_22"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2011.2132790"},{"key":"ref139","article-title":"Highway driving dataset for semantic video segmentation","author":"Kim","year":"2020","journal-title":"arXiv:2011.00674"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2021-248"},{"key":"ref141","article-title":"Compression of deep convolutional neural networks for fast and low power mobile applications","author":"Kim","year":"2015","journal-title":"arXiv:1511.06530"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/TELFOR.2018.8612054"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2016.10"},{"key":"ref144","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u1ef3","year":"2016","journal-title":"arXiv:1610.05492"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3002345"},{"key":"ref146","article-title":"Joint attention in autonomous driving (JAAD)","author":"Kotseruba","year":"2016","journal-title":"arXiv:1609.04741"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.34647\/jmv.nr2.id15"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6142-6_7"},{"key":"ref149","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref150","volume-title":"Tesla trails Waymo, cruise and others in self-driving strategy, study claims","author":"Krok","year":"2020"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/IROS55552.2023.10342053"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.23919\/DATE48585.2020.9116476"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS51556.2021.9401730"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00109"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23528-8_31"},{"key":"ref158","article-title":"Learning low-rank approximation for CNNs","author":"Lee","year":"2019","journal-title":"arXiv:1905.10145"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.215"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107763"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-06910-5"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2018.8297378"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2016.XII.042"},{"key":"ref164","article-title":"Single-shot channel pruning based on alternating direction method of multipliers","author":"Li","year":"2019","journal-title":"arXiv:1902.06382"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS52030.2021.00050"},{"key":"ref166","article-title":"Pruning filters for efficient ConvNets","author":"Li","year":"2016","journal-title":"arXiv:1608.08710"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475314"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19839-7_24"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/3DV53792.2021.00130"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00292"},{"key":"ref171","first-page":"1","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3015456"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2973615"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1109\/IGCC.2016.7892585"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00437"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_39"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1145\/3448416"},{"key":"ref179","article-title":"Sparse quantized spectral clustering","author":"Liao","year":"2020","journal-title":"arXiv:2010.01376"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813860"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197440"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00290"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1145\/3173162.3173191"},{"key":"ref184","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"Lin","year":"2017","journal-title":"arXiv:1712.01887"},{"key":"ref185","first-page":"23","article-title":"Power-efficient time-sensitive mapping in heterogeneous systems","volume-title":"Proc. Int. Conf. Parallel Archit. Compilation Techn.","author":"Liu"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2920341"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00816"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3043716"},{"key":"ref189","article-title":"PIRT: A runtime framework to enable energy-efficient real-time robotic applications on heterogeneous architectures","author":"Liu","year":"2018","journal-title":"arXiv:1802.08359"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2915983"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317749"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6520"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533681"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05057-3_35"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.5220\/0010254203620369"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3075291"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12561"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1145\/3446640"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00272"},{"key":"ref202","article-title":"One million scenes for autonomous driving: ONCE dataset","author":"Mao","year":"2021","journal-title":"arXiv:2106.11037"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00315"},{"key":"ref204","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Stat.","volume":"54","author":"McMahan"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01296"},{"key":"ref207","first-page":"129","article-title":"Automotive radar dataset for deep learning based 3D object detection","volume-title":"Proc. 16th Eur. Radar Conf. (EuRAD)","author":"Meyer"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"ref209","article-title":"The StreetLearn environment and dataset","author":"Mirowski","year":"2019","journal-title":"arXiv:1903.01292"},{"key":"ref210","article-title":"Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy","author":"Mishra","year":"2017","journal-title":"arXiv:1711.05852"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207531"},{"key":"ref212","article-title":"6G V2X technologies and orchestrated sensing for autonomous driving","author":"Mizmizi","year":"2021","journal-title":"arXiv:2106.16146"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.3390\/s19071674"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1109\/WCNCW.2019.8902527"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2017.2752798"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2463671"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919489"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/SLT48900.2021.9383593"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS51556.2021.9401657"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20887-5_43"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500547"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref225","article-title":"Direct feedback alignment provides learning in deep neural networks","author":"N\u00f8kland","year":"2016","journal-title":"arXiv:1609.01596"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1109\/ICRAIE51050.2020.9358321"},{"key":"ref227","doi-asserted-by":"publisher","DOI":"10.23919\/MVA51890.2021.9511383"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7140013"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1166\/jolpe.2018.1536"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9413181"},{"key":"ref231","first-page":"3963","article-title":"Learning compact neural networks with regularization","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","volume":"80","author":"Oymak"},{"key":"ref232","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3147324"},{"key":"ref233","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304596"},{"key":"ref235","doi-asserted-by":"publisher","DOI":"10.1145\/3370748.3406585"},{"key":"ref236","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794329"},{"key":"ref237","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8462915"},{"key":"ref238","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref239","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793925"},{"key":"ref240","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794341"},{"key":"ref241","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2833427"},{"key":"ref242","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197385"},{"key":"ref243","doi-asserted-by":"publisher","DOI":"10.5937\/tehnika2102171P"},{"key":"ref244","doi-asserted-by":"publisher","DOI":"10.1177\/0278364920979368"},{"key":"ref245","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00102"},{"key":"ref246","first-page":"652","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Qi"},{"key":"ref247","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","author":"Qi","year":"2017","journal-title":"arXiv:1706.02413"},{"key":"ref248","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2990174"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9501007"},{"key":"ref250","doi-asserted-by":"publisher","DOI":"10.1109\/icra48506.2021.9561663"},{"key":"ref251","first-page":"1","article-title":"ROS: An open-source robot operating system","volume-title":"Proc. ICRA Workshop Open Source Softw.","author":"Quigley"},{"key":"ref252","doi-asserted-by":"publisher","DOI":"10.1117\/12.2548146"},{"key":"ref253","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2020.2970041"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036971"},{"key":"ref255","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"key":"ref256","volume-title":"Use of the 5.850-5.925 GHz Band, ET Docket No. 19\u2013308","year":"2019"},{"key":"ref257","doi-asserted-by":"publisher","DOI":"10.1109\/SOCC.2018.8618557"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638949"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8020117"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2971090"},{"key":"ref261","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9413016"},{"key":"ref262","doi-asserted-by":"publisher","DOI":"10.1109\/CoolChips.2019.8721356"},{"key":"ref263","article-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler","year":"2019","journal-title":"arXiv:1903.02891"},{"key":"ref264","article-title":"A commute in data: The comma2k19 dataset","author":"Schafer","year":"2018","journal-title":"arXiv:1812.05752"},{"key":"ref265","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21854"},{"key":"ref266","doi-asserted-by":"publisher","DOI":"10.23919\/FUSION49465.2021.9627037"},{"key":"ref267","doi-asserted-by":"publisher","DOI":"10.1007\/s10766-019-00645-y"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2014-274"},{"key":"ref269","volume-title":"Industry leaders to form consortium for network and computing infrastructure of automotive big data","year":"2021"},{"key":"ref270","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414831"},{"key":"ref271","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000373"},{"key":"ref272","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6409"},{"key":"ref273","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC51323.2021.9498677"},{"key":"ref274","article-title":"Improving 3D object detection with channel-wise transformer","author":"Sheng","year":"2021","journal-title":"arXiv:2108.10723"},{"key":"ref275","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref276","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref277","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149138"},{"key":"ref278","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3007787"},{"key":"ref279","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054168"},{"key":"ref280","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3046971"},{"key":"ref281","doi-asserted-by":"publisher","DOI":"10.1177\/09544070211016254"},{"key":"ref282","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-27562-4_6"},{"key":"ref283","first-page":"1","article-title":"Starting European field tests for CAR-2-X communication: The drive C2X framework","volume-title":"Proc. 18th ITS World Congr. Exhibit.","author":"Stahlmann"},{"key":"ref284","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2018.8341973"},{"key":"ref285","article-title":"Sparsified SGD with memory","author":"Stich","year":"2018","journal-title":"arXiv:1809.07599"},{"key":"ref286","doi-asserted-by":"publisher","DOI":"10.1109\/latincom.2018.8613206"},{"key":"ref287","doi-asserted-by":"publisher","DOI":"10.1007\/s38311-018-0166-9"},{"key":"ref288","doi-asserted-by":"publisher","DOI":"10.1145\/3410992.3411014"},{"key":"ref289","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3212075"},{"key":"ref290","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref291","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.035"},{"key":"ref292","doi-asserted-by":"publisher","DOI":"10.3390\/s20247344"},{"key":"ref293","doi-asserted-by":"publisher","DOI":"10.1145\/3341105.3373918"},{"key":"ref294","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00036"},{"key":"ref295","doi-asserted-by":"publisher","DOI":"10.1109\/c-code.2019.8680977"},{"key":"ref296","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref297","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.03.174"},{"key":"ref298","first-page":"9356","article-title":"DropNet: Reducing neural network complexity via iterative pruning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref299","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00807"},{"key":"ref300","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2970728"},{"key":"ref301","article-title":"PI-EDge: A low-power edge computing system for real-time autonomous driving services","author":"Tang","year":"2019","journal-title":"arXiv:1901.04978"},{"key":"ref302","doi-asserted-by":"publisher","DOI":"10.1109\/CRV.2018.00032"},{"key":"ref303","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99322-5_14"},{"key":"ref304","first-page":"1","article-title":"eSGD: Communication efficient distributed deep learning on the edge","volume-title":"Proc. USENIX Workshop Hot Topics Edge Comput. (HotEdge)","author":"Tao"},{"key":"ref305","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.695"},{"key":"ref306","doi-asserted-by":"publisher","DOI":"10.1109\/TMBMC.2019.2950182"},{"key":"ref307","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref308","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2020-Fall49728.2020.9348525"},{"key":"ref309","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2017.2723926"},{"key":"ref310","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00145"},{"key":"ref311","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2009.11.005"},{"key":"ref312","article-title":"Six levels of autonomous process execution management (APEM)","author":"van der Aalst","year":"2022","journal-title":"arXiv:2204.11328"},{"key":"ref313","doi-asserted-by":"publisher","DOI":"10.1109\/ITST.2017.7972217"},{"key":"ref314","article-title":"A survey on edge benchmarking","author":"Varghese","year":"2020","journal-title":"arXiv:2004.11725"},{"key":"ref315","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00190"},{"key":"ref316","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533738"},{"key":"ref317","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.13"},{"key":"ref318","first-page":"1","article-title":"PowerSGD: Practical low-rank gradient compression for distributed optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Vogels"},{"key":"ref319","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"ref320","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref321","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636655"},{"key":"ref322","article-title":"Atomo: Communication-efficient learning via atomic sparsification","author":"Wang","year":"2018","journal-title":"arXiv:1806.04090"},{"key":"ref323","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_24"},{"key":"ref324","article-title":"The NEOLIX open dataset for autonomousdriving","author":"Wang","year":"2020","journal-title":"arXiv:2011.13528"},{"key":"ref325","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00073"},{"key":"ref326","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.327"},{"key":"ref327","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486403"},{"key":"ref328","first-page":"627","article-title":"Power-efficient layer mapping for cnns on integrated CPU and GPU platforms: A case study","volume-title":"Proc. 26th Asia South Pacific Design Autom. Conf.","author":"Wang"},{"key":"ref329","doi-asserted-by":"publisher","DOI":"10.3390\/rs13163340"},{"key":"ref330","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800286"},{"key":"ref331","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2018.00010"},{"key":"ref332","article-title":"LaneNet: Real-time lane detection networks for autonomous driving","author":"Wang","year":"2018","journal-title":"arXiv:1807.01726"},{"key":"ref333","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Wen"},{"key":"ref334","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.78"},{"key":"ref335","article-title":"TernGrad: Ternary gradients to reduce communication in distributed deep learning","author":"Wen","year":"2017","journal-title":"arXiv:1705.07878"},{"key":"ref336","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-71278-5_29"},{"key":"ref337","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.60"},{"key":"ref338","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"ref339","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2018.2865359"},{"key":"ref340","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3155724"},{"key":"ref341","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3099597"},{"key":"ref342","first-page":"1","article-title":"AutoPrune: Automatic network pruning by regularizing auxiliary parameters","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS 2019)","volume":"32","author":"Xiao"},{"key":"ref343","article-title":"X-view: Non-egocentric multi-view 3D object detector","author":"Xie","year":"2021","journal-title":"arXiv:2103.13001"},{"key":"ref344","article-title":"Combining deep reinforcement learning and safety based control for autonomous driving","author":"Xiong","year":"2016","journal-title":"arXiv:1612.00147"},{"key":"ref345","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00033"},{"key":"ref346","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.188"},{"key":"ref347","doi-asserted-by":"publisher","DOI":"10.1109\/ICUSAI47366.2019.9124850"},{"key":"ref348","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155438"},{"key":"ref349","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01516"},{"key":"ref350","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564825"},{"key":"ref351","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/136"},{"key":"ref352","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793523"},{"key":"ref353","doi-asserted-by":"publisher","DOI":"10.3390\/s18103337"},{"key":"ref354","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341406"},{"key":"ref355","first-page":"146","article-title":"HDNET: Exploiting HD maps for 3D object detection","volume-title":"Proc. 2nd Annu. Conf. Robot Learn. Mach. Learn. Res.","volume":"87","author":"Yang"},{"key":"ref356","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00798"},{"key":"ref357","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00748"},{"key":"ref358","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"ref359","article-title":"IPOD: Intensive point-based object detector for point cloud","author":"Yang","year":"2018","journal-title":"arXiv:1812.05276"},{"key":"ref360","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00204"},{"key":"ref361","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3037554"},{"key":"ref362","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1900120"},{"key":"ref363","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2968399"},{"key":"ref364","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02065"},{"key":"ref365","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref366","first-page":"1","article-title":"Understanding straight-through estimator in training activation quantized neural nets","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yin"},{"key":"ref367","article-title":"Center-based 3D object detection and tracking","author":"Yin","year":"2020","journal-title":"arXiv:2006.11275"},{"key":"ref368","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00940"},{"key":"ref369","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3063107"},{"key":"ref370","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref371","doi-asserted-by":"publisher","DOI":"10.1109\/NAS.2018.8515731"},{"key":"ref372","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref373","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700105"},{"key":"ref374","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207413"},{"key":"ref375","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412678"},{"key":"ref376","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2983149"},{"key":"ref377","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01231-1_25"},{"key":"ref378","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3088910"},{"key":"ref379","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref380","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139486"},{"key":"ref381","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2947490"},{"key":"ref382","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2597169"},{"key":"ref383","doi-asserted-by":"publisher","DOI":"10.3390\/s21072477"},{"key":"ref384","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2018.00131"},{"key":"ref385","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3016961"},{"key":"ref386","first-page":"1","article-title":"SkyNet: A hardware-efficient method for object detection and tracking on embedded systems","volume-title":"Proc. Mach. Learn. Syst.","author":"Zhang"},{"key":"ref387","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00182"},{"key":"ref388","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3066837"},{"key":"ref389","doi-asserted-by":"publisher","DOI":"10.1109\/IWQoS.2017.7969112"},{"key":"ref390","article-title":"Improving semi-supervised federated learning by reducing the gradient diversity of models","author":"Zhang","year":"2020","journal-title":"arXiv:2008.11364"},{"key":"ref391","article-title":"FedPAGE: A fast local stochastic gradient method for communication-efficient federated learning","author":"Zhao","year":"2021","journal-title":"arXiv:2108.04755"},{"key":"ref392","doi-asserted-by":"publisher","DOI":"10.1109\/ITNEC48623.2020.9085054"},{"key":"ref393","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.04.039"},{"key":"ref394","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3018557"},{"key":"ref395","doi-asserted-by":"publisher","DOI":"10.1145\/3274783.3275199"},{"key":"ref396","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01426"},{"key":"ref397","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/65"},{"key":"ref398","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-017-1750-y"},{"key":"ref399","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref400","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2864289"},{"key":"ref401","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref402","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2918951"},{"key":"ref403","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2800793"},{"key":"ref404","article-title":"Class-balanced grouping and sampling for point cloud 3D object detection","author":"Zhu","year":"2019","journal-title":"arXiv:1908.09492"},{"key":"ref405","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref406","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2018.8486500"},{"key":"ref407","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00826"}],"container-title":["IEEE Communications Surveys &amp; Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9739\/10324457\/10213996.pdf?arnumber=10213996","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T18:49:22Z","timestamp":1709318962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10213996\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":407,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/comst.2023.3302474","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}