Vehicle Driving Intent Recognition Based on Enhanced Bidirectional Long Short-Term Memory Network

Abstract

: In the context of high-speed mixed traffic and intricate multi-vehicle interaction, existing driving intention recognition models for research vehicles inadequately address crucial factors, such as driving style and vehicle-vehicle interaction information. This paper introduces a novel driving intention recognition model based on an enhanced bidirectional long-and short-term memory network (Bi LSTM). The proposed model leverages the driving trajectory sequence of the target vehicle, driving style, and interaction features of surrounding vehicles as inputs for effective training and learning. It facilitates the classification and recognition of the driving intention feature dataset, specifically considering diverse driving styles. Additionally, the whale optimization algorithm is employed to optimize pivotal hyperparameters, encompassing the number of hidden layer nodes and learning rate, effectively mitigating the adverse impacts of manual parameter adjustment. The model's efficacy is validated using the NGSIM dataset, exhibiting an impressive recognition accuracy of 97.5% in precisely identifying vehicle driving intentions.