A Reinforcement Learning-Based Controller Designed for Intersection Signal Suffering from Information Attack

With the rapid development of smart technology and wireless communication technology, Intelligent Transportation System (ITS) is considered as an effective way to solve the traffic congestion problem. ITS is able to collect real-time road vehicle information through sensors such as networked vehicles (CV) and cameras, and through real-time interaction of information, signals can more intelligently implement adaptive signal adjustment, which can effectively reduce vehicle delays and traffic congestion. However, this connectivity also poses new challenges in terms of being affected by malicious attacks that affect traffic safety and efficiency. Reinforcement learning is considered as the future trend of control algorithms for intelligent transportation systems. In this paper, we design reinforcement learning intelligent control algorithms to control the intersection signal imposed by malicious attacks. The results show that the reinforcement learning-based signal control model can reduce vehicle delay and queue length by 22% and 23% relative to timing control. Meanwhile, the intensity learning is a model-free control method, which makes it impossible for attackers to target flaws in specific control logic and evaluate the impact of information attacks more effectively. Designing a coordinated state tampering attack between different lanes, the results show that the impact is greatest when the attacked states are in the same phase.