Author(s): Ahmed Kuwait Mohammed

Abstract: Autonomous driving technology has rapidly advanced in recent years, driven by the growing demand for safer, more efficient transportation systems. A critical aspect of autonomous vehicle functionality is its ability to perceive the surrounding environment accurately and in real time. This study focuses on the integration of real-time distance measurement and object detection systems to enhance situational awareness and improve decision-making in autonomous driving applications. The proposed system utilizes a combination of LiDAR sensors, ultrasonic sensors, and computer vision algorithms powered by convolutional neural networks (CNNs) to detect and classify objects, while simultaneously measuring their distance from the vehicle. The fusion of sensor data allows for precise obstacle recognition and avoidance under varying environmental conditions, including low visibility and dynamic traffic scenarios. Experimental results demonstrate that the system is capable of identifying multiple object types—such as pedestrians, vehicles, and road signs with high accuracy, while maintaining a real-time response speed necessary for autonomous navigation. Additionally, the integration of distance measurement algorithms significantly reduces collision risk by enabling timely braking and maneuvering responses. This research highlights the importance of multi-sensor data fusion and advanced image processing techniques in developing reliable autonomous vehicle systems. The findings support further development and optimization of intelligent driving models that can operate safely in real-world environments. Future work will aim to enhance system performance through deep learning optimization and integration with vehicle-to-everything (V2X) communication technologies.

DOI: 10.22271/27891607.2025.v5.i2a.311

Pages: 33-47 | Views: 751 | Downloads: 177

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