A General Reliability-Aware Fusion Concept Using DST and Supervised Learning with Its Applications in Multi-Source Road Estimation

Concerning the variety of challenging situations of automated driving, a simple average fusion of all available information sources is not appropriate to obtain satisfying results. Hence, this paper presents a novel framework for the road estimation task by incorporating reliability into the multi-source fusion. First, we specify the common JDL fusion model for this task and extend it at multiple levels. Secondly, we integrate an offline-trained knowledge base for the reliability assessment represented by Bayesian Network or Random Forests. Thirdly, we propose a reliability-aware fusion of various sources at the decision level by applying Dempster-Shafer theory. As a result, our system can solve conflict situations among the sources more satisfyingly. Compared to the average fusion, experiments on real world data verify that our concept can increase the overall performance of automated driving up to 8 percentage points.

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