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Multi-Source Fusion for Robust Road Detection Using Online Estimated Reliabilities
M.Sc. Tuan Tran Nguyen
For highly available automated driving, a robust road estimation is indispensable. In order to tackle the challenges of this task, many works employ a fusion of multiple sources, e.g. visually detected lane markings, leading vehicle, digital maps, etc. However, each source has certain advantages and drawbacks depending on the operational scenariosthe assumption made by many existing approaches that the sources always are equally reliable for the fusion process is inappropriate. Therefore, this work in cooperation with VW  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 reliability for each source is online estimated using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, experimental results show that the presented reliability-aware fusion increases the availability of automated driving.

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