# Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles

arXiv: https://arxiv.org/abs/2111.02845 · HTML: https://mlsanity.com/paper/2111.02845/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Adversarial Policies: Attacking Deep Reinforcement Learning](https://mlsanity.com/paper/1905.10615/) (2019)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [Robust Reinforcement Learning on State Observations with Learned Optimal Adversary](https://mlsanity.com/paper/2101.08452/) (2021)
- [Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks](https://mlsanity.com/paper/1701.04143/) (2017)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09684/) (2020)
- [Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning](https://mlsanity.com/paper/1704.08883/) (2017)
- [Deep Deterministic Policy Gradient for Urban Traffic Light Control](https://mlsanity.com/paper/1703.09035/) (2017)
- [Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization](https://mlsanity.com/paper/1909.10651/) (2019)
- [The Faults in Our Pi Stars: Security Issues and Open Challenges in Deep Reinforcement Learning](https://mlsanity.com/paper/1810.10369/) (2018)
- [Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories](https://mlsanity.com/paper/1910.06788/) (2019)

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

- [SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.04299/) (2023)
- [Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems](https://mlsanity.com/paper/2211.01845/) (2022)

## Abstract (arXiv)

The rapid advancements of Internet of Things (IoT) and artificial intelligence (AI) have catalyzed the development of adaptive traffic signal control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) methods produce the state-of-the-art performance and have great potentials for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully "trust" that vehicles are sending the true information to the signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this paper first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to "cheat" DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh, a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our method to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.