# Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles

arXiv: https://arxiv.org/abs/2305.16818 · HTML: https://mlsanity.com/paper/2305.16818/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 9 of 9)

- [Control Barrier Functions for Systems with High Relative Degree](https://mlsanity.com/paper/1903.04706/) (2019)
- [A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2007.08041/) (2020)
- [Bridging the Gap between Optimal Trajectory Planning and Safety-Critical Control with Applications to Autonomous Vehicles](https://mlsanity.com/paper/2008.07632/) (2020)
- [Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Control](https://mlsanity.com/paper/2010.04753/) (2020)
- [Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning](https://mlsanity.com/paper/2103.10533/) (2021)
- [Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments](https://mlsanity.com/paper/2111.09521/) (2021)
- [Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2204.04555/) (2022)
- [On the Security of Networked Control Systems in Smart Vehicle and its Adaptive Cruise Control](https://mlsanity.com/paper/2008.00414/) (2020)
- [Trust-aware Control for Intelligent Transportation Systems](https://mlsanity.com/paper/2111.04248/) (2021)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems](https://mlsanity.com/paper/2403.17338/) (2024)
- [Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow](https://mlsanity.com/paper/2506.12600/) (2025)

## Abstract (arXiv)

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the traffic network. We propose a decentralized resilient control and coordination scheme that mitigates the effects of adversarial attacks and uncooperative CAVs by utilizing a trust framework. Our trust-aware scheme can guarantee safe collision free coordination and mitigate traffic jams. Simulation results validate the theoretical guarantee of our proposed scheme, and demonstrate that it can effectively mitigate adversarial effects across different traffic scenarios.