V. Basil Hans,
- Research Professor, Department of Management & Commerce, Srinivas University, Mangaluru, Karnataka, India
Abstract
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 (light detection and ranging), 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 ]
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):1-10.
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):1-10. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=208523
References
- Velez G, Otaegui O. Embedded platforms for computer vision-based advanced driver assistance systems: a survey. arXiv preprint. arXiv:1504.07442. April 28, 2015.
- Marti E, De Miguel MA, Garcia F, Perez J. A review of sensor technologies for perception in automated driving. IEEE Intell Transport Syst Mag. 2019; 11 (4): 94–108.
- Simhambhatla R, Okiah K, Kuchkula S, Slater R. Self-driving cars: evaluation of deep learning techniques for object detection in different driving conditions. SMU Data Sci Rev. 2019; 2 (1): 23.
- Feng D, Haase-Schütz C, Rosenbaum L, Hertlein H, Glaeser C, Timm F, Wiesbeck W, Dietmayer K. Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Trans Intell Transport Syst. 2020; 22 (3): 1341–1360.
- Weber Y, Kanarachos S. The correlation between vehicle vertical dynamics and deep learning- based visual target state estimation: a sensitivity study. Sensors. 2019; 19 (22): 4870.
- Tang Y, Zhao C, Wang J, Zhang C, Sun Q, Zheng WX, Du W, Qian F, Kurths J. Perception and navigation in autonomous systems in the era of learning: a survey. IEEE Trans Neural Netw Learn Syst. 2022; 34 (12): 9604–9624.
- Mora L, Wu X, Panori A. Mind the gap: developments in autonomous driving research and the sustainability challenge. J Cleaner Prod. 2020; 275: 124087.
- Lim KL, Bräunl T. A review of visual odometry methods and its applications for autonomous driving. arXiv preprint. arXiv:2009.09193. September 19, 2020.
- Liu L, Lu S, Zhong R, Wu B, Yao Y, Zhang Q, Shi W. Computing systems for autonomous driving: State of the art and challenges. IEEE Internet of Things J. 2020; 8 (8): 6469–6486.
- Eising C, Horgan J, Yogamani S. Near-field perception for low-speed vehicle automation using surround-view fisheye cameras. IEEE Trans Intell Transport Syst. 2021; 23 (9): 13976–13993.
- Wang Y, Mao Q, Zhu H, Deng J, Zhang Y, Ji J, Li H, Zhang Y. Multi-modal 3D object detection in autonomous driving: a survey. Int J Computer Vision. 2023; 131: 2122–2152.
- Fernandes D, Afonso T, Girão P, Gonzalez D, Silva A, Névoa R, Novais P, Monteiro J, Melo-Pinto P. Real-time 3D object detection and slam fusion in a low-cost lidar test vehicle setup. Sensors. 2021; 21 (24): 8381.
- Zhang Q, Gou S, Li W. Visual perception system for autonomous driving. In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, October 14–18, 2024. pp. 3204–3211.
- Contreras M, Jain A, Bhatt NP, Banerjee A, Hashemi E. A survey on 3D object detection in real time for autonomous driving. Front Robotics AI. 2024; 11: 1212070.
- Liang L, Ma H, Zhao L, Xie X, Hua C, Zhang M, Zhang Y. Vehicle detection algorithms for autonomous driving: a review. Sensors. 2024; 24 (10): 3088.

Research & Reviews: A Journal of Embedded System & Applications
| Volume | 13 |
| Issue | 02 |
| Received | 14/03/2025 |
| Accepted | 24/03/2025 |
| Published | 22/04/2025 |
| Publication Time | 39 Days |
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