# Trust-aware Control for Intelligent Transportation Systems

arXiv: https://arxiv.org/abs/2111.04248 · HTML: https://mlsanity.com/paper/2111.04248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning](https://mlsanity.com/paper/2004.04450/) (2020)
- [TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data](https://mlsanity.com/paper/1909.07867/) (2019)
- [Cooperative Adaptive Cruise Control for a Platoon of Connected and Autonomous Vehicles Considering Dynamic Information Flow Topology](https://mlsanity.com/paper/1807.02224/) (2018)

## Built on by (most cited first, 1 of 1)

- [Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles](https://mlsanity.com/paper/2305.16818/) (2023)

## Abstract (arXiv)

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.