A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources / Tuan Tran Nguyen.

To tackle the challenges of the road estimation task, many works employ a fusion of multiple sources. By that, a commonly made assumption is that the sources always are equally reliable. However, this assumption is inappropriate since each source has certain advantages and drawbacks depending on the...

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Bibliographic Details
Main Author: Nguyen, Tuan Tran
Format: eBook
Language:English
Published: Wiesbaden : Springer, 2019.
Series:AutoUni-Schriftenreihe ; Bd. 140.
Subjects:
Online Access:Click for online access

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245 1 2 |a A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources /  |c Tuan Tran Nguyen. 
260 |a Wiesbaden :  |b Springer,  |c 2019. 
300 |a 1 online resource (180 pages) 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a AutoUni-Schriftenreihe ;  |v Band 140 
546 |a Abstract in English and German. 
504 |a Includes bibliographical references. 
588 0 |a Print version record. 
505 0 |a Reliability-Aware Fusion Framework -- Assessing and Learning Reliability for Ego-Lane Estimation -- Reliability-Based Ego-Lane Estimation Using Multiple Sources. 
520 |a To tackle the challenges of the road estimation task, many works employ a fusion of multiple sources. By that, a commonly made assumption is that the sources always are equally reliable. However, this assumption is inappropriate since each source has certain advantages and drawbacks depending on the operational scenarios. Therefore, Tuan Tran Nguyen proposes a novel concept by incorporating reliabilities into the multi-source fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the author estimates the reliability for each source online using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, he shows via experimental results that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion. Contents Reliability-Aware Fusion Framework Assessing and Learning Reliability for Ego-Lane Estimation Reliability-Based Ego-Lane Estimation Using Multiple Sources Target Groups Scientists and students in the fields of IT, fusion and automated driving Engineers working in industrial research and development of automated driving About the Author Tuan Tran Nguyen received the Master's degree in computer science and the Ph. D. degree from Otto-von-Guericke University Magdeburg, Germany, in 2013 and 2019, respectively. His research focuses on methods and architectures for reliability-based sensor fusion in intelligent vehicles. 
650 0 |a Context-aware computing. 
650 0 |a Wireless sensor networks. 
650 7 |a Context-aware computing  |2 fast 
650 7 |a Wireless sensor networks  |2 fast 
776 0 8 |i Print version:  |a Nguyen, Tuan Tran.  |t A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources.  |d Wiesbaden : Springer, ©2019  |z 9783658269487 
830 0 |a AutoUni-Schriftenreihe ;  |v Bd. 140. 
856 4 0 |u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-658-26949-4  |y Click for online access 
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