?url_ver=Z39.88-2004&rft_id=TR2018-FIT-05&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=https%3A%2F%2Feprints.uet.vnu.edu.vn%2Feprints%2Fid%2Feprint%2F3197%2F&rft.title=Deep+learning+based+detection+of+vehicles%2C+lane+and+street+sign+for+behavior+cloning+in+autonomous+car&rft.creator=Nguyen%2C+Huu+Nhat+Minh&rft.creator=Phan%2C+Xuan+Hieu&rft.creator=Tran%2C+Quoc+Long&rft.subject=Information+Technology+(IT)&rft.description=With+the+growth+of+Artificial+intelligence+and+Machine+learning%2C+the+amount+of+research+for+autonomous+vehicle+is+also+growing+nonstop.+But+a+self-driving+system+is+far+too+complex+with+the+hardware+integration%2C+LiDAR+and+RADAR+involvement%2C+how+exactly+are+these+machine+learning+algorithms+being+applied%2C+where+can+a+machine+learning+researcher+start+to+research+self-driving+car.+This+report+will+cover+how+traditional+machine+learning+methods+and+state+of+the+are+deep+learning+(semantic+segmentation%2C+convolutional+neural+network+-+CNN)+are+being+applied+to+build+autonomous+cars.+The+algorithm+uses+image+processing+techniques+to+label+lane+pixels%2C+classical+machine+learning+and+Histogram+of+Oriented+Gradient+(HOG)+to+label+vehicle+and+street+sign+pixels.+Then+the+data+is+used+to+train+a+semantic+segmentation+network+to+extract+features+for+a+final+CNN+to+combine+with+the+original+image+and+predict+driving+command.&rft.publisher=VNU-UET&rft.date=2018-12&rft.type=Technical+Report&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.language=en&rft.identifier=https%3A%2F%2Feprints.uet.vnu.edu.vn%2Feprints%2Fid%2Feprint%2F3197%2F1%2FFIT-TR2018-NguyenHuuNhatMinh-PhanXuanHieu.pdf&rft.identifier=++Nguyen%2C+Huu+Nhat+Minh+and+Phan%2C+Xuan+Hieu+and+Tran%2C+Quoc+Long++(2018)+Deep+learning+based+detection+of+vehicles%2C+lane+and+street+sign+for+behavior+cloning+in+autonomous+car.++Technical+Report.+VNU-UET.+++++&rft.relation=TR2018-FIT-05