# A Truth-Oriented Trust Evaluation Model of Shared Traffic Messages in the Internet of Vehicles

## TL;DR

This paper introduces a trust model for vehicle messages in the Internet of Vehicles, inspired by human trust mechanisms, to improve traffic safety and decision-making.

## Contribution

A novel truth-oriented trust evaluation model (HS-TEM) is proposed, integrating self-experience and peer-recommendation trust mechanisms.

## Key findings

- The HS-TEM model effectively evaluates message credibility by fusing self-experience and peer-based trust.
- Simulation results show HS-TEM improves fairness and accuracy in trust evaluation for vehicle messages.
- The model reduces individual bias and instability in trust assessment under small-sample conditions.

## Abstract

The Internet of Vehicles (IoV) provides an effective solution for alleviating traffic congestion and enhancing road safety. However, shared traffic messages in IoV may deviate from on-road conditions due to self-interest protection or insufficient sensor performance. Therefore, evaluating the trustworthiness of shared messages is essential for vehicles to make informed decisions. To this end, a truth-oriented trust model for shared traffic message is proposed, which is inspired by human trust establishment mechanisms (HS-TEMs). Firstly, we quantify the integrated trust value (I-VT) of the message sender by fusing self-experience-based vehicle trust (SEB-VT) and peer-recommendation-based vehicle trust (PRB-VT). In SEB-VT, a sample-size-dependent smoothing factor dynamically trades off prior information and empirical evidence, reducing instability under small-sample conditions. In PRB-VT, we employ link analysis to compute the reference degree of recommendation information, which mitigates biases arising from subjective cognitive limitations. Secondly, with the I-VT of vehicles, we calculate event trust (ET) by differentiating message attitudes and quantifying their relative influence, which effectively reduces the impact of individual bias on the final judgment. The simulation results show that HS-TEM can accurately and fairly evaluate the credibility of messages, which helps vehicles make informed decisions.