Research on Lane Change Strategy considering Driver's Personalized Driving Behavior

In view of the problem that human-drivers in the side lanes are prone to enter the team during the driving process of internet vehicle fleets, this paper proposes a personalized driver lane change prediction model based on modern statistical learning theory. In this paper, a lane changing model cons...

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Bibliographic Details
Published in2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 317 - 321
Main Authors Wang, Yi, Deng, Bo, Ou, Yang, Li, Zhe, Fan, Jie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2021
Subjects
Analytical models
Data models
driving bahavior
hidden Markov model
Hidden Markov models
lane change
Markov processes
Predictive models
Random processes
real-world data analysis
Statistical learning
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Summary:In view of the problem that human-drivers in the side lanes are prone to enter the team during the driving process of internet vehicle fleets, this paper proposes a personalized driver lane change prediction model based on modern statistical learning theory. In this paper, a lane changing model considering driver characteristics is constructed by combining the Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM). This method solves the difficulty of describing the static distribution characteristics and dynamic random process in driver behavior. Finally, the lateral acceleration experiment is designed to collect vehicle acceleration data, and the validity of the model structure is verified by using natural driving data.
DOI:10.1109/AIEA53260.2021.00074