Abstract: Autonomous vehicles are the future development direction of the global automotive industry and are essential for promoting the high-quality development of China's independent automotive industry. Autonomous vehicles rely on advanced technologies such as artificial intelligence and ubiquitous sensing to enhance or replace the driver's cognitive perception, decision-making planning, and control execution. Real time, accurate, and robust perception of the road environment is the cornerstone of automotive intelligence, and the huge leap in the field of autonomous vehicles perception in the past decade has been mostly driven by deep learning technology. A review of the development of autonomous vehicles environmental awareness technology in recent years is provided. Firstly, it summarizes the software and hardware architecture of autonomous vehicles environmental awareness systems, and provides an overall overview of perception, computing devices, and algorithm deployment platforms; Secondly, milestone research methods and technical solutions in recent years were summarized around four key tasks: object detection and semantic segmentation, multi objects tracking, object intention recognition and trajectory prediction, and environmental mapping; Finally, the problems and challenges faced by current autonomous vehicles environmental perception system were analyzed, and future development trends and key technologies were prospected.