Object Detection in Autonomous Driving with Sensor-Based Technology Using YOLOv10

The creation of intelligent transportation systems, such as autonomous driving and traffic monitoring, is dependent on precise vehicle recognition. Autonomous vehicles detect and recognize objects in real-time, such as pedestrians, other vehicles, traffic signs, and obstacles.  This paper improves the object detection ability of autonomous vehicles (AVs') by integrating technologies including YOLOv10 and multi-modal sensor fusion. This paper  takes a deep learning algorithm with  sensor technology, about important issues in the areas of response time, real-time processing, and detection accuracy. They have used YOLOv10's architectural and optimization strategies along with a comprehensive methodology that integrates data from LiDAR, radar, and cameras to construct a trustworthy perception system for dynamic and flexible driving settings. According to the experimental results, YOLOv10 outperformed both previous versions and competing object detection models with a significantly high accuracy of 96.8%, while maintaining a processing speed of 80 frames per second. Additionally, YOLOv10 had a significantly higher recall of 94.1%, and an accuracy of 95.4%, indicating its increased effectiveness at pedestrian and obstacle identification in the autonomous driving domain. With explicit attention to accounting for occlusions and poor lighting, the authors created a strong and scalable framework for deep learning to bridge the gap between theory and application in autonomous driving. Furthermore, the extension to address these issues enhances reliability and safety in autonomous systems and will ultimately aid the development and adoption of broader autonomous technology systems.