Research on control strategy of multi-source data fusion solar intelligent vehicle based on image recognition

Research on suitability evaluation and layout strategy of indemnificatory housing site selection based on multi-source data in Shenzhen City

Lulin Zhang

International Journal of Low-Carbon Technologies, 2021, vol. 16, issue 4, 1363-1370

Abstract: Nowadays, the global energy and environmental problems are becoming more and more serious, which promotes the development and utilization of renewable and clean energy in various countries. Intelligent car involves many subjects such as electronic technology, artificial intelligence, automatic control technology, sensor technology and computer technology and has become an important part of the application of artificial intelligence. Solar cell is a necessary part of the normal operation of the solar intelligent car, which can provide clean energy for the intelligent car. In this paper, the image recognition technology is used to design the intelligent vehicle control system. According to the intelligent vehicle path recognition, the scale invariant feature transform (SIFT) algorithm is improved to improve the accuracy of intelligent vehicle recognition. Data fusion is used to process the data detected by multi-sensor, and the running state of intelligent vehicle is studied. An evaluation method of intelligent vehicle navigation parameters based on association rules and belief network is proposed. The maximum power point tracking control is realized by using the interference observation method to ensure that the intelligent vehicle can track the maximum power point of the solar cell.

Keywords: SIFT algorithm; multi-source data fusion; image recognition; solar smart car (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctab057 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:16:y:2021:i:4:p:1363-1370.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().