Driving Intention Recognition Model for Highway Ramp-Merging Scene

<div>Driving safety in the mixed traffic state of autonomous vehicles and conventional vehicles has always been an important research topic, especially on highways where autonomous driving technology is being more widely adopted. The merging scenario at highway ramps poses high risks with frequent vehicle conflicts, often stemming from misperceived intentions [<span>1</span>].</div> <div>This study focuses on autonomous and conventional vehicles in merging scenarios, where timely recognition of lane-changing intentions can enhance merging efficiency and reduce accidents. First, trajectory data of merging vehicles and their conflicting vehicles were extracted from the NGSIM open-source database in the I-80 section. The segmented cubic polynomial interpolation method and Savitzky–Golay filtering are utilized for data outlier removal and noise reduction. Second, the processed trajectory data were used as input to a hybrid Gaussian hidden Markov (GMM-HMM) model for driving intention classification, specifically lane-change collision-avoidance and lane keeping. The K-means algorithm is used to initialize the model parameters, and the expectation–maximization (EM) algorithm is employed for parameter iteration. Finally, through validation on the testing set, the mixed Gaussian hidden Markov model achieves a lane-change intention recognition accuracy of over 95% for conflicting vehicles and outperforms the support vector machines (SVM) model and the long–short-term memory (LSTM) network. It can be applied to the humanized design of intelligent vehicle lane-change strategies, effectively reducing lane-change risks and improving driving safety.</div>