Correct driving decisions are a prerequisite for safe driving. In this paper, we construct a lane-changing decision model considering driving style in order to provide drivers with more accurate and timely lane-changing decision interventions. Based on NGSIM data, two-step trajectory reconstruction technique is firstly used to deal with data outliers and noise, and then vehicle lane-changing trajectory extraction and phase classification are carried out according to the definition of lane-changing decision, and k-means clustering method is also used to realize driving style calibration and determine lane-changing decision variables based on following phase data. The final XGBoost lane-changing decision model was constructed, and the need to incorporate driving style features and the superiority of XGBoost over RF and SVM models were verified through comparative experiments.
Research on lane-changing decision model with driving style based on XGBoost
Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023) ; 2023 ; Xi'an, China
Proc. SPIE ; 12989
2024-04-09
Conference paper
Electronic Resource
English
XGBoost Lane-Changing Decision Model Considering Driving Style
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