Self-Driving Cars and Computer Vision: Enhancing Computer Vision for Autonomous Vehicle Navigation

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 13 | Issue : 02 | Page : –
    By

    V. Basil Hans,

  1. Research Professor, Department of Management & Commerce, Srinivas University, Mangaluru, Karnataka, India

Abstract

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 Autonomous vehicles, commonly known as self-driving cars, are transforming the transportation sector by aiming to enhance road safety, ease traffic congestion, and boost overall efficiency. Central to the operation of these vehicles is computer vision, which enables them to perceive and understand their environment. This paper examines how computer vision contributes to the navigation of autonomous vehicles and highlights its continuous developments. Specifically, it examines key challenges such as real-time object detection, lane detection, and environmental mapping, as well as the integration of various sensors like cameras, LiDAR, and radar. Additionally, we explore how deep learning algorithms, especially convolutional neural networks (CNNs), contribute to improving object recognition and obstacle detection. We also explore the limitations and ethical considerations of relying on computer vision for navigation, including issues related to sensor fusion, decision-making under uncertainty, and the impact of adverse weather conditions on performance. Finally, the paper highlights emerging trends and future directions in computer vision for autonomous vehicles, including multi- modal learning, edge computing, and the need for more robust and scalable vision systems. As self- driving technology continues to evolve, advancements in computer vision will be crucial for realizing the full potential of autonomous vehicles in everyday traffic scenarios.

Keywords: Traffic scenarios, autonomous vehicles, detect obstacles, sensor quality, Self-driving cars

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]

How to cite this article:
V. Basil Hans. Self-Driving Cars and Computer Vision: Enhancing Computer Vision for Autonomous Vehicle Navigation. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(02):-.
How to cite this URL:
V. Basil Hans. Self-Driving Cars and Computer Vision: Enhancing Computer Vision for Autonomous Vehicle Navigation. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(02):-. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=0



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Regular Issue Subscription Review Article
Volume 13
Issue 02
Received 14/03/2025
Accepted 24/03/2025
Published 22/04/2025
Publication Time 39 Days
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