Car-following behavior modeling driven by small data sets based on mnemonic extreme gradient boosting framework  

作　　者：Baichuan LOU Yufang LI Xiaoding LU Zhe XU 

机构地区：[1]Department of Vehicle Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China [2]Key Laboratory of Advanced Manufacture Technology for Automobile Parts(Chongqing University of Technology),Ministry of Education,Chongqing 400000,China

出　　处：《Science China(Information Sciences)》2022年第6期267-268,共2页中国科学（信息科学）（英文版）

基　　金：supported by Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts,Ministry of Education,China(Grant No.2019KLMT05);Natural Science Foundation of Chongqing(Grant No.cstc2019jcyj-msxmX0119)。

摘　　要：Dear editor,In recent years,data-driven car-following models have been developed based on their ability to drill down to information in driving data and their flexibility.According to a study by Fleming et al.[1],the main limitation of existing methods is an insufficient amount of natural driving data.In addition,there are many situations during actual driving processes,such as those under extreme conditions[2]and in the early stages of car-following behavior modeling.In these cases,sparse data learning algorithms are indispensable.

关 键 词：BOOSTING EDITOR driving 

分 类 号：U463.6[机械工程—车辆工程]