| AUTOMOTIVE ENGINEERING |
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Overview of deep learning intelligent driving methods |
| ZHANG Xinyu1, GAO Hongbo2, ZHAO Jianhui3, ZHOU Mo2 |
1. Information Technology Center, Tsinghua University, Beijing 100084, China;
2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;
3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China |
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Abstract This paper introduces target recognition and detection methods based on the convolutional neural network (CNN) model, the improved regions with convolutional neural network (R-CNN) and the task-assistant convolutional neural network (TA-CNN) model for pedestrian detection. This paper also describes stereo matching based on a deep learning model for stereo matching using the Siamese network. Multi-source data fusion is also introduced based on a vision sensor, a radar sensor and a camera using a deep learning network. The CNN is used for end-to-end horizontal and vertical control of autonomous vehicles. Deep learning is widely used in the perception level, decision-making level and control level in automatic driving systems to continuously improve the perception, detection, decision-making and control accuracy. Analyses show that deep learning will improve of autonomous driving systems.
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| Keywords
computer vision
deep learning
autonomous vehicle
sensor
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Issue Date: 15 April 2018
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2018,
Vol. 58
