Distributed Unmanned Vehicle Platoon Control: A Combining Intention Recognition Method

This paper aims to improve the safety of road traffic and design a vehicle platoon controller based on the vehicle lane-changing intention recognition model. Through deep learning technology, the lane-changing intention of surrounding vehicles on the road is detected in real-time. First, we used the Generative Adversarial Networks (GAN) network to increase the number of lane-changing data in the original data, and train a deep neural network model based on the Long Short-Term Memory (LSTM) network, with the model accuracy reaching 93.54%. Based on the intent recognition model, the leading vehicle adjusts the longitudinal distance from the surrounding vehicles according to the probability of the detected intent to change lanes, to ensure the safety of the platoon during the lane-changing process of the surrounding vehicles. Finally, the related simulation verification is carried out by Matlab/Simulink joint Prescan, and the experimental results show that the proposed method recognizes a difference of 0.4 s between the time of generating lane change intention and the actual time.