Research on lane-changing decision model with driving style based on XGBoost

Shuao Zhang; Xiaoming Shao; Jiangfeng Wang

doi:10.1117/12.3023862

9 April 2024 Research on lane-changing decision model with driving style based on XGBoost

Shuao Zhang, Xiaoming Shao, Jiangfeng Wang

Proceedings Volume 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023); 129890E (2024) https://doi.org/10.1117/12.3023862
Event: Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 2023, Xi'an, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuao Zhang, Xiaoming Shao, and Jiangfeng Wang "Research on lane-changing decision model with driving style based on XGBoost", Proc. SPIE 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 129890E (9 April 2024); https://doi.org/10.1117/12.3023862

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available