Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder

作     者：Xin HE Zhe ZHANG Li XU Jiapei YU Xin HE;Zhe ZHANG;Li XU;Jiapei YU

作者机构：College of Electrical EngineeringZhejiang UniversityHangzhou 310027China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2022年第23卷第3期

页      面：452-462页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 080902[工学-电路与系统] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Ford Motor Company(No.URP 2018-J077.4)。 

主　　题：Driving behavior Normalization Gated auto-encoder Quantitative evaluation 

摘      要：Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition.Unlike the model-based approach as in previous work,where a necessary driver model is employed to conduct the driving cycle test,the approach we propose directly normalizes the driving behavior using an autoencoder(AE)when following a standard speed profile.To ensure a positive correlation between the vehicle speed and driving behavior,a gate constraint is imposed in between the encoder and decoder to form a gated AE(gAE).This approach is model-free and efficient.The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis.Simulations are conducted to verify the effectiveness of the proposed scheme.