Analysis of Robustness in Lane Detection using Machine Learning Models

Analysis of Robustness in Lane Detection using Machine Learning Models

Bibliographic Details
Main Author: Adams, William A.
Language:English
Published: Ohio University / OhioLINK 2015
Subjects:
Artificial Intelligence
Automotive Engineering
Engineering
Computer Science
Machine Learning
ADAS
Lane Detection
Autoencoder
Regressor
Deep Network
Deep Learning
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449167611
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ohiou14491676112021-08-03T06:34:22Z Analysis of Robustness in Lane Detection using Machine Learning Models Adams, William A. Artificial Intelligence Automotive Engineering Engineering Computer Science Machine Learning ADAS Lane Detection Autoencoder Regressor Deep Network Deep Learning An appropriate approach to incorporating robustness into lane detection algorithms is beneficial to autonomous vehicle applications and other problems relying on fusion methods. While traditionally rigorous empirical methods were developed for mitigating lane detection error, an evidence-based model-driven approach yields robust results using multispectral video as input to various machine learning models.Branching beyond the few network structures considered for image understanding applications, deep networks with unique optimization functions are demonstrably more robust while making fewer assumptions. This work adopts a simple framework for data collection; retrieving image patches for comparison via regression through a learning model. Along a horizontal scanline, the most probable sample is selected to retrain the network. Models include simple regressors, various autoencoders, and a few specialized deep networks. Samples are compared by robustness and the results favor deep and highly specialized network structures. 2015 English text Ohio University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449167611 http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449167611 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Artificial Intelligence
Automotive Engineering
Engineering
Computer Science
Machine Learning
ADAS
Lane Detection
Autoencoder
Regressor
Deep Network
Deep Learning
spellingShingle Artificial Intelligence
Automotive Engineering
Engineering
Computer Science
Machine Learning
ADAS
Lane Detection
Autoencoder
Regressor
Deep Network
Deep Learning
Adams, William A.
Analysis of Robustness in Lane Detection using Machine Learning Models
author Adams, William A.
author_facet Adams, William A.
author_sort Adams, William A.
title Analysis of Robustness in Lane Detection using Machine Learning Models
title_short Analysis of Robustness in Lane Detection using Machine Learning Models
title_full Analysis of Robustness in Lane Detection using Machine Learning Models
title_fullStr Analysis of Robustness in Lane Detection using Machine Learning Models
title_full_unstemmed Analysis of Robustness in Lane Detection using Machine Learning Models
title_sort analysis of robustness in lane detection using machine learning models
publisher Ohio University / OhioLINK
publishDate 2015
url http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449167611
work_keys_str_mv AT adamswilliama analysisofrobustnessinlanedetectionusingmachinelearningmodels
_version_ 1719439306812030976

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