Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology

TL;DR

This paper investigates how deep learning enhances autonomous driving by improving image recognition, target tracking, environment perception, and navigation, demonstrating high accuracy and real-time performance improvements.

Contribution

It introduces a comprehensive deep learning-based system for autonomous driving, integrating CNNs, multi-task learning, and reinforcement learning to improve key functionalities.

Findings

Image recognition accuracy over 98%

Efficient environment perception and decision support

Enhanced real-time response capabilities

Abstract

This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential.