Trust-aware Control for Intelligent Transportation Systems

TL;DR

This paper introduces a trust-aware control framework for intelligent transportation systems, utilizing epistemic logic to quantify trust and reinforcement learning to enhance safety and efficiency in multi-agent traffic management.

Contribution

It presents a novel trust quantification framework integrated into transportation control, and demonstrates its effectiveness through a trust-aware autonomous intersection management case study.

Findings

Trust-aware controllers reduce accident rates.

Trust quantification improves agent coordination.

Enhanced safety in mixed trusted and untrusted agent scenarios.

Abstract

Many intelligent transportation systems are multi-agent systems, i.e., both the traffic participants and the subsystems within the transportation infrastructure can be modeled as interacting agents. The use of AI-based methods to achieve coordination among the different agents systems can provide greater safety over transportation systems containing only human-operated vehicles, and also improve the system efficiency in terms of traffic throughput, sensing range, and enabling collaborative tasks. However, increased autonomy makes the transportation infrastructure vulnerable to compromised vehicular agents or infrastructure. This paper proposes a new framework by embedding the trust authority into transportation infrastructure to systematically quantify the trustworthiness of agents using an epistemic logic known as subjective logic. In this paper, we make the following novel contributions: (i) We propose a framework for using the quantified trustworthiness of agents to enable trust-aware coordination and control. (ii) We demonstrate how to synthesize trust-aware controllers using an approach based on reinforcement learning. (iii) We comprehensively analyze an autonomous intersection management (AIM) case study and develop a trust-aware version called AIM-Trust that leads to lower accident rates in scenarios consisting of a mixture of trusted and untrusted agents.