  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.
