eprintid: 3197
rev_number: 7
eprint_status: archive
userid: 290
dir: disk0/00/00/31/97
datestamp: 2018-12-08 08:50:20
lastmod: 2018-12-08 08:50:20
status_changed: 2018-12-08 08:50:20
type: monograph
metadata_visibility: show
creators_name: Nguyen, Huu Nhat Minh
creators_name: Phan, Xuan Hieu
creators_name: Tran, Quoc Long
creators_id: hieupx@vnu.edu.vn
creators_id: tqlong@vnu.edu.vn
title: Deep learning based detection of vehicles, lane and street sign for behavior cloning in autonomous car
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: With the growth of Artificial intelligence and Machine learning, the amount of research for autonomous vehicle is also growing nonstop. But a self-driving system is far too complex with the hardware integration, LiDAR and RADAR involvement, how exactly are these machine learning algorithms being applied, 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, convolutional neural network - CNN) are being applied to build autonomous cars. The algorithm uses image processing techniques to label lane pixels, 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.
date: 2018-12
date_type: completed
publisher: VNU-UET
id_number: TR2018-FIT-05
full_text_status: public
monograph_type: technical_report
pages: 8
institution: VNU University of Engineering and Technology
department: Faculty of Information Technology
citation:   Nguyen, Huu Nhat Minh and Phan, Xuan Hieu and Tran, Quoc Long  (2018) Deep learning based detection of vehicles, lane and street sign for behavior cloning in autonomous car.  Technical Report. VNU-UET.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3197/1/FIT-TR2018-NguyenHuuNhatMinh-PhanXuanHieu.pdf