Abstract:
Within the context of road estimation, the present paper addresses the problem of the fusion of several sources with different reliabilities. Thereby, reliability represe...Show MoreMetadata
Abstract:
Within the context of road estimation, the present paper addresses the problem of the fusion of several sources with different reliabilities. Thereby, reliability represents a higher-level uncertainty. This problem arises in automated driving and ADAS due to changing environmental conditions, e.g., road type or visibility of lane markings. Thus, we present an online sensor reliability assessment and reliability-aware fusion to cope with this challenge. First, we apply a boosting algorithm to select the highly discriminant features among the extracted information. Using them we apply different classifiers to learn the reliabilities, such as Bayesian Network and Random Forest classifiers. To stabilize the estimated reliabilities over time, we deploy approaches such as Dempster-Shafer evidence theory and Influence Diagram combined with a Bayes Filter. Using a big collection of real data recordings, the experimental results support our proposed approach.
Published in: 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Date of Conference: 16-18 November 2017
Date Added to IEEE Xplore: 11 December 2017
ISBN Information: