This paper is based on the study of incorporating personalized driver styles in automatic lane changing control technology, to establish a vehicle lane changing trajectory planning and control model considering driver styles as well as to improve the applicability of the lane changing planning control model to drivers with different styles. First, the HighD dataset is screened, and drivers are categorized according to their driving styles using principal component analysis and K-means (K-means) cluster analysis. Secondly, the process of generating drivers' lane changing decisions is fully considered, and human drivers' driving behavior experience is learned through Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) methods that introduce the attention mechanism, so as to improve drivers' acceptance and satisfaction of smart vehicle acceptance and satisfaction, and propose a lane changing decision model that considers the rationality and safety of lane changing. An emergency lane changing control strategy based on Model Predictive Control (MPC) is adopted, and finally, a joint simulation is conducted by Simulink/CarSim software to prove that the lane changing decision-making method based on the driver's style proposed in this study is able to realize the autonomous lane changing task of intelligent vehicles.

Shenhao Hou, Fulei Liu. Behavior-Based Anthropomorphic Lane-Changing Decision and Control for Intelligent Vehicles on Highways. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 11: 105-111. https://doi.org/10.25236/AJETS.2023.061116.

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