AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (637.8 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Short Communication | Open Access

Physics-informed machine learning for autonomous driving: From perception to control

Yubing Yang1Meng Li2Yuhao Wang3( )
School of Information Engineering, Chang’an University, Xi’an 710064, China
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Show Author Information

References

【1】
【1】
 
 
Journal of Intelligent and Connected Vehicles
Article number: 9210077

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Yang Y, Li M, Wang Y. Physics-informed machine learning for autonomous driving: From perception to control. Journal of Intelligent and Connected Vehicles, 2026, 9(1): 9210077. https://doi.org/10.26599/JICV.2026.9210077

1611

Views

135

Downloads

0

Crossref

0

Web of Science

0

Scopus

Received: 15 December 2025
Revised: 04 January 2026
Accepted: 20 January 2026
Published: 31 March 2026
© The Author(s) 2026.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0 http://creativecommons.org/licenses/by/4.0/).