Application of a Multifunctional Image Processing System Based on C in Autonomous Driving

Autonomous driving technology is a current research hotspot in the fields of artificial intelligence and computer vision. Its core relies on environmental information obtained from sensors such as cameras and radars. Image processing technology plays a crucial role in autonomous driving, including tasks such as lane detection, obstacle recognition, and environmental perception. With the rapid development of autonomous driving technology, the demand for image processing systems has significantly increased, especially in terms of real-time performance, accuracy, and multifunctionality. Existing image processing tools are mostly single-functional, making it difficult to meet the complex and varied demands of autonomous driving scenarios. Therefore, developing a system that integrates multiple image processing functions can effectively enhance the environmental perception capabilities of autonomous driving systems and provide reliable data support for subsequent path planning and decision-making. This study developed a multifunctional image processing system based on C, focusing on the system's architecture, module division, and algorithm implementation. Experimental results show that the system can effectively improve the environmental perception capabilities of autonomous driving systems and perform well in terms of processing efficiency and user satisfaction.