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

TL;DR

This work addresses how deep learning can elevate computer-vision–driven autonomous driving by integrating CNN-based image recognition, multi-task tracking, Double DQN–driven perception and decision-making, and graph-based path planning with an A* heuristic. It presents a unified DL pipeline that achieves high accuracy (>98%) in image recognition, tracking, and navigation, with environmental perception within ~97.8% accuracy, and maintains millisecond-level response times across components. Key methods include a VGGNet-like CNN for detection, a multi-task loss for tracking, a Double DQN framework for decision support, and Graph Neural Networks with A* routing for routing under dynamic conditions; these are formalized with equations for learning and planning, such as the Q-learning update $Q(s_t, a_t) = Q(s_t, a_t) + \alpha ( r_{t+1} + \gamma \max_a Q(S_{t+1}, a) - Q(s_t, a_t) )$. The results demonstrate practical, real-time DL-enabled autonomy and point to future improvements via robustness, scalability, and larger pretrained or diffusion-based vision models to further enhance safety and reliability.

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.

Figures (2)

• Figure 1: Principle of autonomous driving technology
• Figure 2: Principle of computer vision system