Research on the Application of Recognition and Detection Technology in Automatic Driving

Abstract

Object detection is the cornerstone of autonomous driving systems. In autonomous driving systems, vehicle detection is critical to maintaining traffic flow and avoiding collisions. The use of deep learning technology enables the system to skillfully and accurately identify vehicles on the road. Deep learning algorithms can provide autonomous vehicles with precise environmental awareness to enable informed driving decisions. Deep learning algorithms can achieve pedestrian detection. Such algorithms can improve the safety of vehicle operations. Through deep learning models, the system is able to identify and track pedestrians, ensuring appropriate responses in different traffic scenarios. The application of detection technology enables vehicles to promptly identify and interpret road signs and traffic lights in real-time. This recognition ability helps to comprehensively understand traffic conditions and improve the ability to adapt to different road conditions. This study provides an in-depth exploration of the importance and application of deep learning technology in the field of autonomous driving. Special attention is paid to the key role played by object detection methods. and propose future development directions. Deep learning algorithms can expand the application of learning technology to handle real-time traffic conditions and achieve rapid response. Achieve the fusion of multiple sensory information by improving the local processing capabilities of edge computing. This research aims to further improve the level of intelligence inherent in autonomous driving systems.