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| Artikel-Nr.: 858A-9783658269487 Herst.-Nr.: 9783658269487 EAN/GTIN: 9783658269487 |
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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. Weitere Informationen: | | Author: | Tuan Tran Nguyen | Verlag: | Springer Fachmedien Wiesbaden GmbH | Sprache: | eng |
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| Weitere Suchbegriffe: maschinenbau und fertigungstechnik, Robust Ego-Lane Estimation, Road Detection, Multi-Source Fusion, Reliability, Ego-Lane and Estimation, Intelligent Vehicles, Classification, Dempster-Shafer Theory, Learning Reliability, Neural Networks, Random Forests |
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