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