Abstract
In this chapter, we propose a novel reliability estimation concept for ego-lane detection. By that, the reliabilities of the participating sensors are learned and estimated using multiple classification approaches, whose input data consists of different types of information, such as contextual data, sensor measurements. Afterward, we demonstrate the feasibility of our introduced concept by applying it to real data.
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© 2020 Springer Fachmedien Wiesbaden GmbH, part of Springer Nature
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Nguyen, T.T. (2020). Learning Reliability. In: A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources. AutoUni – Schriftenreihe, vol 140. Springer, Wiesbaden. https://doi.org/10.1007/978-3-658-26949-4_5
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DOI: https://doi.org/10.1007/978-3-658-26949-4_5
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Publisher Name: Springer, Wiesbaden
Print ISBN: 978-3-658-26948-7
Online ISBN: 978-3-658-26949-4
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