Abstract
The purpose of this paper is to analyse and discuss the application forms of existing autonomous driving technology with a focus on deep learning, while also describing the opportunities and challenges currently faced by this technology. By combining a literature review, this paper sorts out the components of autonomous driving technology and lists several typical and effective application examples, providing an overview of their development ideas and principles. Examples include traffic sign recognition based on visual technology, pedestrian trajectory prediction, vehicle speed control based on algorithms, and lane-level path planning. Research shows that deep learning technology can significantly improve the accuracy and stability of autonomous driving technology in complex environments. However, this technology is still in the development and exploration stage, with issues such as safety risks and data privacy. In summary, deep learning is indispensable in autonomous driving technology, but its characteristics also determine that mature applications require the support of comprehensive regulations and data systems. Future research should focus on the development of emerging technologies while also concentrating on this aspect to promote the construction of intelligent transportation systems.
Published Version
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