A Subjective-Logic-based Reliability Estimation Mechanism for Cooperative Information with Application to IV's Safety
It addresses the safety-critical problem of data reliability for intelligent vehicles using cooperative perception, an incremental improvement over existing fusion methods.
This paper proposes a novel method using Subjective Logic to estimate the reliability of cooperative information for intelligent vehicles, enabling separation of faulty from correct data with a large safety margin in real-world experiments.
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.