Nishant Varshney,
- Student, Department of Mechanical Engineering, Amity School of Engineering and Technology, Noida, Gautam Buddha Nagar, Uttar Pradesh, India
Abstract
The integration of machine vision in autonomous vehicles (AVs) is a critical advancement in the field of intelligent transportation systems. Machine vision systems enable AVs to perceive their environment, understand road conditions, detect obstacles, and make real-time decisions necessary for safe navigation. These systems rely heavily on image processing techniques, which have evolved significantly over the past decade, leading to improved performance in complex driving scenarios. These developments are largely made possible by deep learning, more especially convolutional neural networks (CNNs), which allow for very accurate and precise object detection and categorization. The ability to process large-scale visual data in real time has become essential for ensuring AVs operate efficiently in dynamic environments, such as city streets and highways. This study examines the evolving role of machine vision in autonomous vehicles, particularly focusing on recent advancements in real-time image processing. We explore the technological challenges faced by AV systems, including limitations imposed by environmental factors such as lighting conditions and sensor reliability. Additionally, the study reviews current machine vision techniques like object detection, semantic segmentation, and multi-modal sensor fusion. Finally, we explore future directions, including improvements in robustness, edge computing for real-time processing, and adversarial machine learning, which aims to enhance the safety and security of AVs. Through an in-depth literature review, this study provides insights into how machine vision is transforming autonomous driving and shaping the future of transportation.
Keywords: Autonomous vehicle, multi modal sensor fusion, CNN, semantic segmentation, artificial intelligence
[This article belongs to Trends in Machine design ]
Nishant Varshney. Role of Machine Vision in Autonomous Vehicles: A Review. Trends in Machine design. 2025; 12(01):38-43.
Nishant Varshney. Role of Machine Vision in Autonomous Vehicles: A Review. Trends in Machine design. 2025; 12(01):38-43. Available from: https://journals.stmjournals.com/tmd/article=2025/view=209183
References
- Turk MA, Morgenthaler DG, Gremban KD, Marra VITS-a vision system for automated land vehicle navigation. IEEE Trans Pattern Anal Mach Intell. 1988 May; 10(3): 342–361.
- Asmika, Boosarapu M, Rani PS. Deep learning for vision and decision making in self-driving cars—challenges with ethical decision making. 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India. 2021. pp. 1-5. DOI: 10.1109/CONIT51480.2021.9498342.
- Litman T. Autonomous vehicle implementation predictions: Implications for transport planning. Victoria Transport Policy Institute; 2024 Oct 23.
- Anderson JM, Nidhi K, Stanley KD, Sorensen P, Samaras C, Oluwatola OA. Autonomous vehicle technology: A guide for policymakers. CA: Rand Corporation; 2014 Jan 10.
- Patnaik A, Tiwari P. On the Realization of Autonomous cars in diverse conditions. Int J Appl Innov Eng Manag. 2014 Sep; 3(9): 262–267.
- Moore MM, Lu B. Autonomous vehicles for personal transport: A technology assessment. Available at SSRN 1865047. 2011 Jun 2.
- Ahmad F, Kazim M, Adnane A, Awad A. Vehicular cloud networks: Architecture, applications and security issues. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC). 2015 Dec 7; 571–576.
- Ballesteros JJ. Circulación de vehículos autónomos. Retos legislativos. Carreteras: Revista técnica de la Asociación Española de la Carretera. 2017; (216): 18–27.
- Barth M, Boriboonsomsin K. Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transp Res D: Transp Environ. 2009 Aug 1; 14(6): 400–10.
- De Almeida Correia GH, van Arem B. Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility. Transp Res B: Methodol. 2016 May 1; 87: 64–88.
Trends in Machine design
| Volume | 12 |
| Issue | 01 |
| Received | 14/02/2025 |
| Accepted | 17/02/2025 |
| Published | 01/03/2025 |
| Publication Time | 15 Days |
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