Deep Learning Application and Optimization in Autonomous Driving Environment Sensing

With the rapid development of autonomous driving technology, environment perception becomes a key link to ensur vehicle safety and decision effectiveness. Deep learning technology, with its powerful data processing and pattern recognition capabilities, has been widely used in automatic driving environment perception. This paper systematically explores the basic applications of deep learning in environment perception, including image recognition, target detection and semantic segmentation. In addition, this paper also analyzes in depth the strategies to optimize the application of deep learning technology in environment perception, which mainly include data enhancement and preprocessing, model architecture optimization, and multi-sensor fusion technology. For the application challenges of autonomous driving in complex environments, such as dynamic scenes, uncertainty and extreme weather, this paper proposes a series o solutions, including reinforcement learning, transfer learning and simulation training. Through the research in this paper, we aim to provide reference and guidance for the future development of autonomous driving environment perception technology, and promote the realization of more efficient and safe autonomous driving systems.