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A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources

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A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources

Tuan Tran Nguyen

Springer
Collection: AutoUni – Schriftenreihe
Publication date : 2019-06-14

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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.

About the Author:

Tuan Tran Nguyen received the Master's degree incomputer 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.

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About the book

Author

Tuan Tran Nguyen

Imprint

Springer

Collection

AutoUni – Schriftenreihe

Publication date

2019-06-14

Pages

164 pages

Print ISBN

9783658269487

Language

English

Ebook informations

EAN PDF

9783658269494

Price

£34.99

Copy count 1

Print count 16

File size 9462 Ko

Informations accessibility

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About author(s)

Tuan Tran Nguyen

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