ABSTRACT Cooperative adaptive cruise control, also known as vehicular platooning, has gained significant interest for its ability to enhance fuel efficiency and comfort in vehicle operations. This study proposes novel control strategies for vehicular platooning based on long short‐term memory (LSTM) neural networks. By learning temporal dependencies in vehicle behaviour, the proposed LSTM‐based controllers improve string stability within the platoon, particularly under varying velocity patterns of the lead vehicle. Two distinct frameworks are investigated: centralized and decentralized control models. The centralized model makes use of the states of all vehicles within the platoon, whereas the decentralized model focuses on the states of only a limited number of preceding vehicles. Simulation experiments demonstrate that both the centralized and decentralized LSTM controllers significantly outperform traditional, non‐LSTM‐based controllers in minimizing cumulative inter‐vehicle error. This study contributes a novel controller training methodology that integrates LSTM‐based architectures with optimal control principles, offering improved adaptability and flexibility in real‐time platoon management.
Nakai et al. (Thu,) studied this question.