Summary of Research on the Application Of Computer Vision Based on Deep Learning in Autonomous Driving Technology, by Jingyu Zhang et al.
Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
by Jingyu Zhang, Jin Cao, Jinghao Chang, Xinjin Li, Houze Liu, Zhenglin Li
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research explores the application of deep learning in autonomous driving computer vision technology, focusing on improving system performance. The study employs advanced technologies like convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning to analyze image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. The proposed system achieves an accuracy of over 98% in image recognition, target tracking, and classification, demonstrating efficient performance and practicality in environmental perception and decision support, as well as path planning and navigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how deep learning can help make self-driving cars better. It uses special computer programs to recognize images, track targets, and make decisions quickly. The study found that these programs are very accurate – over 98% correct! This means they can help self-driving cars make good choices on the road. |
Keywords
» Artificial intelligence » Classification » Cnn » Deep learning » Multi task » Reinforcement learning » Tracking
