{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T10:36:40Z","timestamp":1726137400861},"publisher-location":"Cham","reference-count":57,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030779382"},{"type":"electronic","value":"9783030779399"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77939-9_1","type":"book-chapter","created":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T19:26:52Z","timestamp":1633116412000},"page":"1-24","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review"],"prefix":"10.1007","author":[{"given":"Alaa","family":"Khamis","sequence":"first","affiliation":[]},{"given":"Dipkumar","family":"Patel","sequence":"additional","affiliation":[]},{"given":"Khalid","family":"Elgazzar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Zafarifar B,Weda H et\u00a0al (2008) Horizon detection based on sky-color and edge features. In: Visual communications and image processing, vol\u00a06822. International Society for Optics and Photonics, p\u00a0682220","DOI":"10.1117\/12.766689"},{"issue":"10","key":"1_CR2","doi-asserted-by":"publisher","first-page":"2216","DOI":"10.3390\/s19102216","volume":"19","author":"W Zhan","year":"2019","unstructured":"Zhan W, Xiao C, Wen Y, Zhou C, Yuan H, Xiu S, Zhang Y, Zou X, Liu X, Li Q (2019) Autonomous visual perception for unmanned surface vehicle navigation in an unknown environment. Sensors 19(10):2216","journal-title":"Sensors"},{"key":"1_CR3","unstructured":"Rebetez J, Satiz\u00e1bal HF, Mota M, Noll D, B\u00fcchi L, Wendling M, Cannelle, B, P\u00e9rez-Uribe A, Burgos S (2016) Augmenting a convolutional neural network with local histograms-a case study in crop classification from high-resolution UAV imagery. ESANN"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Hui X, Bian J, Zhao X, Tan M (2018) Deep-learning-based autonomous navigation approach for uav transmission line inspection. In: 10th international conference on advanced computational intelligence (ICACI). IEEE, pp\u00a0455\u2013460","DOI":"10.1109\/ICACI.2018.8377502"},{"key":"1_CR5","unstructured":"Khamis A (2019) Biological versus non-biological\/artificial intelligence. In: Towards data science"},{"issue":"5","key":"1_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/scientificamericanmind0917-21","volume":"28","author":"A Gopnik","year":"2017","unstructured":"Gopnik A (2017) An ai that knows the world like children do. Scientific Amer. Mind 28(5):21\u201328","journal-title":"Sci Am Mind"},{"key":"1_CR7","unstructured":"Mitchell T (1997) Machine learning. McGraw-Hill"},{"issue":"2","key":"1_CR8","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MIS.2009.36","volume":"24","author":"A Halevy","year":"2009","unstructured":"Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intelligent Systems 24(2):8\u201312","journal-title":"IEEE Intell Syst"},{"key":"1_CR9","unstructured":"Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol\u00a01. MIT Press, Cambridge"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, et\u00a0al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","DOI":"10.1038\/nature14236"},{"issue":"7","key":"1_CR11","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural computation 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Meyer D (2015) Introduction to autoencoders","DOI":"10.1007\/978-3-662-45837-2_1"},{"key":"1_CR13","unstructured":"Carreira-Perpinan MA, HintonGE (2005) On contrastive divergence learning. In: Aistats, vol\u00a010. Citeseer, pp\u00a033\u201340"},{"key":"1_CR14","unstructured":"Michigan (2019) Michigan self driving car dataset. Accessed 11 Oct 2020"},{"key":"1_CR15","unstructured":"Oxford (2014) Oxford robot car car dataset. Accessed 11 Oct 2020"},{"key":"1_CR16","unstructured":"Stanford (2009) Stanford self driving car dataset. Accessed 11 Oct 2020"},{"key":"1_CR17","unstructured":"Udacity (2016) Udacity self driving car dataset. Accessed 11 Oct 2020"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Pitropov M, GarciaD, Rebello J, Smart M, Wang C, Czarnecki K, Waslander S (2020) Canadian adverse driving conditions dataset.arXiv preprint arXiv:2001.10117","DOI":"10.1177\/0278364920979368"},{"key":"1_CR19","unstructured":"Waymo (2019) Waymo self driving car dataset. Accessed 11 Oct 2020"},{"key":"1_CR20","unstructured":"Scape A (2018) Apollo Scape self driving car dataset. Accessed 11 Oct 2020"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the kitti dataset. Int J Robot Res (IJRR)","DOI":"10.1177\/0278364913491297"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Khamis A (2021) Smart mobility: exploring foundational technologies and wider impacts. APress (Springer Nature), ISBN: 978-1-4842-7101-8","DOI":"10.1007\/978-1-4842-7101-8_1"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Endsley MR (2016) Designing for situation awareness: an approach to user-centered design. CRC Press","DOI":"10.1201\/b11371"},{"key":"1_CR24","unstructured":"Council NR et al (2012) NASA space technology roadmaps and priorities: restoring NASA\u2019s technological edge and paving the way for a new era in space. National Academies Press"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Draper V (1994) Environmental restoration and waste management program teleoperator hand controllers: contextual human factors assessment. OAK Ridge National Laboratory, Departamento de Energia de los Estados Unidos, Reporte","DOI":"10.2172\/10160431"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Bayat B, Bermejo-Alonso J, Carbonera J, Facchinetti T, Fiorini S, Goncalves P, Jorge VA, Habib M, Khamis A, Melo K et\u00a0al (2016) Requirements for building an ontology for autonomous robots. Ind Robot Int J 43(5)","DOI":"10.1108\/IR-02-2016-0059"},{"key":"1_CR27","unstructured":"Hollnagel E (2009) The four cornerstones of resilience engineering"},{"key":"1_CR28","unstructured":"International S (2016) Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE Int (J3016)"},{"issue":"3","key":"1_CR29","doi-asserted-by":"publisher","first-page":"2539","DOI":"10.1109\/LRA.2018.2808368","volume":"3","author":"S Jung","year":"2018","unstructured":"Jung S, Hwang S, Shin H, Shim DH (2018) Perception, guidance, and navigation for indoor autonomous drone racing using deep learning. IEEE Robotics and Automation Letters 3(3):2539\u20132544","journal-title":"IEEE Robot Automat Lett"},{"key":"1_CR30","unstructured":"Kim D, Ryu H, Yonchorhor J, Shim DH (2020) A deep-learning-aided automatic vision-based control approach for autonomous drone racing in game of drones competition. In: NeurIPS competition and demonstration track. PMLR, pp\u00a037\u201346"},{"key":"1_CR31","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.comcom.2015.08.010","volume":"73","author":"A Mammeri","year":"2016","unstructured":"Mammeri A, Boukerche A, Tang Z (2016) A real-time lane marking localization, tracking and communication system. Computer Communications 73:132\u2013143","journal-title":"Comput Commun"},{"key":"1_CR32","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.patcog.2015.12.010","volume":"59","author":"J Niu","year":"2016","unstructured":"Niu J, Lu J, Xu M, Lv P, Zhao X (2016) Robust lane detection using two-stage feature extraction with curve fitting. Pattern Recognition 59:225\u2013233","journal-title":"Patt Recognit"},{"key":"1_CR33","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.compeleceng.2015.01.002","volume":"42","author":"S-C Yi","year":"2015","unstructured":"Yi S-C, Chen Y-C, Chang C-H (2015) A lane detection approach based on intelligent vision. Computers & Electrical Engineering 42:23\u201329","journal-title":"Comput Electri Eng"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Perng JW, Hsu YW, Yang YZ, Chen CY, Yin TK (2020) Development of an embedded road boundary detection system based on deep learning. In: Image and vision computing, p\u00a0103935","DOI":"10.1016\/j.imavis.2020.103935"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Liu B, Liu H, Yuan J (2019) Lane line detection based on mask R-CNN. In: 3rd international conference on mechatronics engineering and information technology (ICMEIT). Atlantis Press","DOI":"10.2991\/icmeit-19.2019.111"},{"key":"1_CR36","unstructured":"Wang Z, Ren W, Qiu Q (2018) Lanenet: real-time lane detection networks for autonomous driving. arXiv preprint arXiv:1807.01726"},{"key":"1_CR37","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","volume":"87","author":"J Kim","year":"2017","unstructured":"Kim J, Kim J, Jang G-J, Lee M (2017) Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Networks 87:109\u2013121","journal-title":"Neural Netw"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Chen Z, Liu Q, Lian C (2019) Pointlanenet: efficient end-to-end CNNS for accurate real-time lane detection. In: IEEE intelligent vehicles symposium (IV). IEEE, pp\u00a02563\u20132568","DOI":"10.1109\/IVS.2019.8813778"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Ma Y, Havyarimana V, Bai J, Xiao Z (2018) Vision-based lane detection and lane-marking model inference: a three-step deep learning approach. In: 9th international symposium on parallel architectures, algorithms and programming (PAAP). IEEE, pp\u00a0183\u2013190","DOI":"10.1109\/PAAP.2018.00039"},{"key":"1_CR40","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.trc.2019.10.011","volume":"109","author":"S Lee","year":"2019","unstructured":"Lee S, Xie K, Ngoduy D, Keyvan-Ekbatani M (2019) An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction. Transportation research part C: emerging technologies 109:117\u2013136","journal-title":"Transp Res Part C Emerging Technol"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Zakaria N, Shapiai M, Rahman MA, Yahya W (2020) Lane line detection via deep learning based-approach applying two types of input into network model. J Soc Automot Eng Malaysia 4(2)","DOI":"10.56381\/jsaem.v4i2.40"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Nassar A, Amer K, ElHakim R, ElHelw M (2018) A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp\u00a01513\u20131523","DOI":"10.1109\/CVPRW.2018.00201"},{"issue":"2","key":"1_CR43","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3390\/rs9020100","volume":"9","author":"MB Bejiga","year":"2017","unstructured":"Bejiga MB, Zeggada A, Nouffidj A, Melgani F (2017) A convolutional neural network approach for assisting avalanche search and rescue operations with uav imagery. Remote Sensing 9(2):100","journal-title":"Remote Sensing"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Liu T, Fu HY, Wen Q, Zhang DK, Li LF (2018) Extended faster R-CNN for long distance human detection: Finding pedestrians in UAV images. In: IEEE international conference on consumer electronics (ICCE). IEEE, pp\u00a01\u20132","DOI":"10.1109\/ICCE.2018.8326306"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Cherian A, Andersh J, Morellas V, Papanikolopoulos N, Mettler B (2009)Autonomous altitude estimation of a UAV using a single onboard camera. In: IEEE\/RSJ international conference on intelligent robots and systems. IEEE, pp\u00a03900\u20133905","DOI":"10.1109\/IROS.2009.5354307"},{"key":"1_CR46","unstructured":"Bart\u00e1k R, Vomlelov\u00e1 M (2017) Using machine learning to identify activities of a flying drone from sensor readings. In: 13th international flairs conference"},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Delmerico J, Giusti A, Mueggler E, Gambardella LM, Scaramuzza D (2016) On-the-spot training for terrain classification in autonomous air-ground collaborative teams. In: International symposium on experimental robotics Springer, pp\u00a0574\u2013585","DOI":"10.1007\/978-3-319-50115-4_50"},{"key":"1_CR48","doi-asserted-by":"crossref","unstructured":"Qu C, Gai W, Zhong M, Zhang J (2020) A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVS) path planning. Appl Soft Comput 8","DOI":"10.1016\/j.asoc.2020.106099"},{"issue":"3","key":"1_CR49","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/j.automatica.2013.12.035","volume":"50","author":"B Zhang","year":"2014","unstructured":"Zhang B, Liu W, Mao Z, Liu J, Shen L (2014) Cooperative and geometric learning algorithm (cgla) for path planning of uavs with limited information. Automatica 50(3):809\u2013820","journal-title":"Automatica"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Junell JL, Van\u00a0Kampen EJ, de\u00a0Visser CC, Chu QP (2015) Reinforcement learning applied to a quadrotor guidance law in autonomous flight. In: AIAA guidance, navigation, and control conference, p\u00a01990","DOI":"10.2514\/6.2015-1990"},{"issue":"2","key":"1_CR51","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/LRA.2018.2795643","volume":"3","author":"A Loquercio","year":"2018","unstructured":"Loquercio A, Maqueda AI, Del-Blanco CR, Scaramuzza D (2018) Dronet: Learning to fly by driving. IEEE Robotics and Automation Letters 3(2):1088\u20131095","journal-title":"IEEE Robot Automat Lett"},{"key":"1_CR52","unstructured":"Yang S, Konam S, Ma C, Rosenthal S, Veloso M, Scherer S (2017) Obstacle avoidance through deep networks based intermediate perception. arXiv preprint arXiv:1704.08759"},{"key":"1_CR53","doi-asserted-by":"crossref","unstructured":"Schultz AC, Grefenstette JJ (2000) Continuous and embedded learning in autonomous vehicles: Adapting to sensor failures. In: Unmanned ground vehicle technology II, vol\u00a04024. International Society for Optics and Photonics, pp\u00a055\u201362","DOI":"10.1117\/12.391649"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Kira Z, Schultz AC (2006) Continuous and embedded learning for multi-agent systems. In: IEEE\/RSJ international conference on intelligent robots and systems. IEEE, pp\u00a03184\u20133190","DOI":"10.1109\/IROS.2006.282343"},{"key":"1_CR55","unstructured":"Ahlawat A, Kabir KS, Pathak K, Singh S, Kaushal S, Kumar S Smart surveillance using on cloud machine learning and internet controlled UAVS"},{"issue":"12","key":"1_CR56","doi-asserted-by":"publisher","first-page":"9524","DOI":"10.1109\/TGRS.2019.2927393","volume":"57","author":"B Kellenberger","year":"2019","unstructured":"Kellenberger B, Marcos D, Lobry S, Tuia D (2019) Half a percent of labels is enough: Efficient animal detection in uav imagery using deep cnns and active learning. IEEE Transactions on Geoscience and Remote Sensing 57(12):9524\u20139533","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"1_CR57","doi-asserted-by":"crossref","unstructured":"Panico A, Fragonara LZ, Al-Rubaye S (2020) Adaptive detection tracking system for autonomous uav maritime patrolling. In: IEEE 7th international workshop on metrology for aerospace (MetroAeroSpace), pp\u00a0539\u2013544","DOI":"10.1109\/MetroAeroSpace48742.2020.9160214"}],"container-title":["Studies in Computational Intelligence","Deep Learning for Unmanned Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77939-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T23:47:07Z","timestamp":1673394427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77939-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779382","9783030779399"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77939-9_1","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}