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

## TL;DR

This paper introduces a Subjective-Logic-based method to estimate the reliability of cooperative information in intelligent vehicles, enhancing safety by effectively fusing multiple data sources and identifying faulty data.

## Contribution

It presents a novel SL-based reliability estimation mechanism that improves data fusion and fault detection in cooperative vehicle perception systems.

## Key findings

- Effective separation of faulty and correct data samples.
- Successful real-world validation of the proposed method.
- Enhanced safety margins in vehicle data reliability assessment.

## Abstract

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.