MPC‐Bi‐LSTM based control strategy for connected and automated vehicles platoon oriented to cyberattacks

Abstract

Abstract Any technological innovation will be accompanied by new challenges and risks, and the connected and automated vehicles (CAVs) are no exception. Among them, the argument that cooperating platoons may fall victim to cyberattacks through wireless communication has emerged as a significant issue. Therefore, this paper designs a communication topology anomaly response system (CTARS) to ensure platoon safety, which consists of a trigger module and a control module. The primary objective of the trigger module is to distinguish abnormal vehicle behavior based on time to collision (TTC) indicators, and the control module combines the model predictive control (MPC) and bidirectional long short‐term memory (Bi‐LSTM) to achieve accurate trajectory prediction of and optimal control, working in tandem with the trigger module. Subsequently, the real dataset HISTORIC is used to calibrate the multiple vehicle intelligent driver model (IDM) and train the trajectory prediction model. Furthermore, comparative simulations are conducted, encompassing various forms of cyberattacks, in order to examine the evolution characteristics of CAVs platoons (CAVP) and evaluate the performance of CTARS. The results demonstrate the remarkable effectiveness of CTARS in safeguarding the security of CAVP during cyberattacks, thus confirming its exceptional performance.

Keywords

• automated driving & intelligent vehicles
• big data
• management and control
• traffic control
• traffic modeling
• vehicle dynamics and control