Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains

Chao Lu, Chen Lv, Jianwei Gong*, Wenshuo Wang, Dongpu Cao, Fei Yue Wang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

10 引用 (Scopus)

摘要

Driver model adaptation (DMA) plays an essential role for driving behaviour modelling when there is a lack of sufficient data for training the new model. A new data-driven DMA method is proposed in this paper to realise the instance-level knowledge transfer between individual drivers. Using the importance-weighted transfer learning (IWTL), the data collected from one driver (source driver) can be directly used to train the model of another driver (target driver). Under the framework of IWTL, the relationship between two different drivers can be modelled by the importance weight (IW). Two estimation methods Kullback-Leibler (KL) Divergence and least-squares (LS), are used to estimate IW for each data instance by modelling the importance-weight function as a radial basis function (RBF). Experiments based on the driving simulator and real vehicle are carried out to test the performance of TL for steering behaviour adaptation during the overtaking manoeuvre. The experimental results show that the TL method can transfer the knowledge observed from one driver to another when training the new driver model without sufficient data by keeping the modelling error at a low level.

源语言英语
页(从-至)17015-17026
页数12
期刊IEEE Transactions on Intelligent Transportation Systems
23
10
DOI
出版状态已出版 - 1 10月 2022

指纹

探究 'Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains' 的科研主题。它们共同构成独一无二的指纹。

引用此