Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains-Reference-Cited by-同舟云学术

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

Published:2022-10 Issue:10 Volume:23 Page:17015-17026
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Lu Chao¹^ORCID,Lv Chen²^ORCID,Gong Jianwei¹^ORCID,Wang Wenshuo³^ORCID,Cao Dongpu⁴^ORCID,Wang Fei-Yue⁵^ORCID

Affiliation:

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

2. School of Mechanical and Aerospace Engineering and the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

3. Department of Civil Engineering, McGill University, Montreal, Canada

4. School of Vehicle and Mobility, Tsinghua University, Beijing, China

5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Technological Innovation Program of the Beijing Institute of Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9916643/09745808.pdf?arnumber=9745808

Reference38 articles.

1. Motion Primitives Representation, Extraction and Connection for Automated Vehicle Motion Planning Applications

2. Importance Weighted Import Vector Machine for Unsupervised Domain Adaptation

3. Nonparametric estimation via empirical risk minimization

4. Importance Weighted Inductive Transfer Learning for Regression

5. Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations

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