Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains
Lu, Chao1; Lv, Chen2,3; Gong, Jianwei1; Wang, Wenshuo4; Cao, Dongpu5; Wang, Fei-Yue6
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2022-03-31
Pages12
Corresponding AuthorGong, Jianwei(gongjianwei@bit.edu.cn)
AbstractDriver 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.
KeywordVehicles Adaptation models Data models Hidden Markov models Knowledge transfer Transfer learning Training Driver behaviour driver model adaptation transfer learning importance weight
DOI10.1109/TITS.2022.3161939
WOS KeywordSTEERING MODEL ; BEHAVIOR ; RECOGNITION ; OVERTAKING ; ASSISTANCE ; VEHICLES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61703041] ; National Natural Science Foundation of China[U19A2083] ; Technological Innovation Program of the Beijing Institute of Technology (BIT)
Funding OrganizationNational Natural Science Foundation of China ; Technological Innovation Program of the Beijing Institute of Technology (BIT)
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000777297900001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48278
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorGong, Jianwei
Affiliation1.Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
2.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
3.Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
4.McGill Univ, Dept Civil Engn, Montreal, PQ H3A 0C3, Canada
5.Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100190, Peoples R China
6.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Lu, Chao,Lv, Chen,Gong, Jianwei,et al. Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:12.
APA Lu, Chao,Lv, Chen,Gong, Jianwei,Wang, Wenshuo,Cao, Dongpu,&Wang, Fei-Yue.(2022).Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12.
MLA Lu, Chao,et al."Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):12.
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