Journal of Advances in Developmental Research

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Using Computer Vision for Advanced Driver Assistance Systems (ADAS) in Autonomous Vehicles

Author(s) Ravikanth Konda
Country United States
Abstract Advanced Driver Assistance Systems (ADAS) have emerged as a critical technology in enhancing vehicle safety, providing convenience to drivers, and facilitating the transition toward fully autonomous vehicles. The core of these systems relies heavily on computer vision (CV) technologies, which enable vehicles to interpret and understand their surrounding environment in real time. The integration of computer vision in ADAS allows vehicles to recognize and process various objects such as pedestrians, vehicles, traffic signs, and lane markings, significantly improving both safety and the driving experience. In this paper, we explore the pivotal role of computer vision in the development and implementation of ADAS in autonomous vehicles. We begin by reviewing the evolution of ADAS, detailing the different sub-systems such as lane departure warning, automatic emergency braking, adaptive cruise control, and parking assistance, all of which depend on sophisticated image processing techniques.
Furthermore, the paper examines the intersection of computer vision with other emerging technologies such as machine learning, deep learning, and sensor fusion. We discuss how these innovations enable ADAS to function more effectively in complex, dynamic environments. The methodology section focuses on cutting-edge algorithms used for object detection, semantic segmentation, and real-time image processing, which are integral to the functionality of modern ADAS. Additionally, we evaluate the challenges that computer vision systems face in real-world applications, including issues with sensor calibration, environmental conditions, computational limitations, and the integration of data from multiple sensors (e.g., cameras, LiDAR, radar).
The paper also presents an analysis of the results obtained from various real-world implementations of ADAS technologies, highlighting improvements in driver safety and the reduction of traffic-related accidents. We explore the potential of computer vision in enabling fully autonomous driving by addressing the current gaps in technology and the advancements needed to close these gaps. Finally, the paper concludes with a discussion on the future of ADAS and the role of computer vision in realizing fully autonomous, reliable, and efficient transportation systems. We argue that continued research and development in computer vision, combined with innovations in deep learning and sensor technologies, are critical to advancing the capabilities and reliability of autonomous vehicles in the near future.
Field Engineering
Published In Volume 15, Issue 1, January-June 2024
Published On 2024-02-07
Cite This Using Computer Vision for Advanced Driver Assistance Systems (ADAS) in Autonomous Vehicles - Ravikanth Konda - IJAIDR Volume 15, Issue 1, January-June 2024. DOI 10.71097/IJAIDR.v15.i1.1407
DOI https://doi.org/10.71097/IJAIDR.v15.i1.1407
Short DOI https://doi.org/g9hjcj
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