Research and Analysis of Deep Learning in Environmental Perception for Autonomous Driving

Abstract

Autonomous driving technology, as a revolutionary development in the transportation field, relies on efficient and reliable environmental perception systems. Deep learning plays a key role in the environmental perception of autonomous driving, with its capabilities widely applied in areas such as signal and sign recognition, semantic segmentation, instance segmentation, and SLAM (Simultaneous Localization and Mapping). This study conducts research and analysis on the aforementioned key technologies. It concludes that while deep learning technology has shown excellent performance in the perceptual systems of autonomous driving, it still faces challenges that require further research and resolution, such as generalization performance, eal-time requirements, and safety issues.