mlsanityData to 13 Oct 2026

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

arXiv 2111.02845 ↗First posted 4 Nov 2021cs.LG cs.AI cs.CRcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 4 Nov 2021.

Builds on

14 of 37 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 14,592
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,439
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,223
  4. 2019Adversarial Policies: Attacking Deep Reinforcement Learningcited by 212
  5. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 159
  6. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 135
  7. 2017Vulnerability of Deep Reinforcement Learning to Policy Induction Attackscited by 91
  8. 2019Learning Phase Competition for Traffic Signal Controlcited by 84
  9. 2020Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learningcited by 61
  10. 2017Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learningcited by 33
  11. 2017Deep Deterministic Policy Gradient for Urban Traffic Light Controlcited by 30
  12. 2019Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimizationcited by 12
2 more
  1. 2018The Faults in Our Pi Stars: Security Issues and Open Challenges in Deep Reinforcement Learningcited by 5
  2. 2019Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectoriescited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learningcited by 3
  2. 2022Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systemscited by 1