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Use of cooperative information, distributed by road-side units, offers largepotential for intelligent vehicles (IVs). As vehicle automation progresses andcooperative perception is used to fill the blind spots of onboard sensors, thequestion of reliability of the data becomes increasingly important in safetyconsiderations (SOTIF, Safety of the Intended Functionality).  This paper addresses the problem to estimate the reliability of cooperativeinformation for in-vehicle use. We propose a novel method to infer thereliability of received data based on the theory of Subjective Logic (SL).Using SL, we fuse multiple information sources, which individually only providemild cues of the reliability, into a holistic estimate, which is statisticallysound through an end-to-end modeling within the theory of SL.  Using the proposed scheme for probabilistic SL-based fusion, IVs are able toseparate faulty from correct data samples with a large margin of safety. Realworld experiments show the applicability and effectiveness of our approach.

A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety