Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm

Wang, S; Kodagoda, S; Shi, L; Dai, X

Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm

Wang, S Kodagoda, S

Shi, L

Dai, X

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Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2018, 33 (1), pp. 29 - 39
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Shi, L https://orcid.org/0000-0002-5215-025X	en_US
dc.contributor.author	Dai, X	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IEEE Intelligent Systems, 2018, 33 (1), pp. 29 - 39	en_US
dc.identifier.issn	1541-1672	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117900
dc.description.abstract	© 2001-2011 IEEE. Road terrain identification is one of the important tasks for driving assistant systems or autonomous land vehicles. It plays a key role in improving driving strategy and enhancing fuel efficiency. In this paper, a two-stage approach using multiple sensors is presented. In the first stage, a feature-based identification approach is performed using an accelerometer, a camera, and downward-looking and forward-looking laser range finders (LRFs). This produces four classification label sequences. In the second stage, a majority vote is implemented for each label sequences to match them into synchronized road patches. Then a Markov Random Field (MRF) model is designed to generate the final optimized identification results to improve the forward-looking LRF. This approach enables the vehicle to observe the upcoming road terrain before moving onto it by fusing all the classification results using an MRF algorithm. The experiments show this approach improved the terrain identification accuracy and robustness significantly for some familiar road terrains.	en_US
dc.relation.ispartof	IEEE Intelligent Systems	en_US
dc.relation.isbasedon	10.1109/MIS.2017.2581327	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	33	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 2001-2011 IEEE. Road terrain identification is one of the important tasks for driving assistant systems or autonomous land vehicles. It plays a key role in improving driving strategy and enhancing fuel efficiency. In this paper, a two-stage approach using multiple sensors is presented. In the first stage, a feature-based identification approach is performed using an accelerometer, a camera, and downward-looking and forward-looking laser range finders (LRFs). This produces four classification label sequences. In the second stage, a majority vote is implemented for each label sequences to match them into synchronized road patches. Then a Markov Random Field (MRF) model is designed to generate the final optimized identification results to improve the forward-looking LRF. This approach enables the vehicle to observe the upcoming road terrain before moving onto it by fusing all the classification results using an MRF algorithm. The experiments show this approach improved the terrain identification accuracy and robustness significantly for some familiar road terrains.

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http://hdl.handle.net/10453/117900