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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM48164

A Comprehensive Review Paper on Computer Vision Applications in Electric Vehicles Using Artificial Intelligence

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM48164

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM48164

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RMGS

Rohit Mungali1 Gayatry Sharma2

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Abstract

The integration of electric vehicles (EVs) with artificial intelligence and its advanced technologies is reshaping the automotive industries, with enhancing the capability, features, efficiency, improving safety and user experience. The key applications of computer vision such as autonomous navigation, focusing on object detection, lane detection, and advanced driver assistance system (ADAS) and energy management are discussed. With use of computer vision technique and AI (deep learning) algorithm, electric vehicle can accurately detect objects, including cars, motor bikes, pedestrians and cyclists that enabling the vehicle respond sophistically. AI’s capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes[1]. And, the paper aims to cover various applications that shows the contribution of AI in electric vehicle to shaping future of intelligence electric vehicles.

KEYWORDS

Vehicles Computer vision electric vehicle IoT Advanced driver assistance systems ADAS EVs

Cite This Article

Rohit Mungali1 Gayatry Sharma2, "A Comprehensive Review Paper on Computer Vision Applications in Electric Vehicles Using Artificial Intelligence," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM48164

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Publication Date

5/17/2025

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Double-Blind Peer Review

Review Rounds: 2

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