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A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources (Paperback, 1st ed. 2020)
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A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources (Paperback, 1st ed. 2020)
Series: AutoUni - Schriftenreihe, 140
Expected to ship within 10 - 15 working days
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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 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.
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