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

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Synopsis

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 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.

Book details

Edition:
1st ed. 2020
Series:
AutoUni – Schriftenreihe (Book 140)
Author:
Tuan Tran Nguyen
ISBN:
9783658269494
Related ISBNs:
9783658269487
Publisher:
Springer Fachmedien Wiesbaden
Pages:
N/A
Reading age:
Not specified
Includes images:
No
Date of addition:
2019-07-21
Usage restrictions:
Copyright
Copyright date:
2020
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Nonfiction, Technology