A Collaborative Study of Perception and Decision-Making Using Machine Learning in Autonomous Vehicles

Self-driving cars are one of the most talked-about frontier areas in today's technology field.This article conducts an in-depth study of machine learning's collaboration of perception and decision-making in autonomous vehicles, aiming to improve the performance, safety, and reliability of autonomous vehicles.The synergy of perception systems and decision-making systems is critical to realizing the vision of autonomous driving.On the perception side, this paper examines the application of various sensor technologies and deep learning algorithms, including target detection, road modeling, and obstacle recognition.In terms of decision-making, this paper focuses on path planning, control algorithms, and safety.In addition, this paper analyzes the impact of perception errors on decision-making and the importance of data sharing and integration.The synergistic effect of perception and decision-making was verified through experiments and case studies, and future research directions were discussed based on the experimental results.The research results of this article provide strong support for the further development of autonomous driving technology and are expected to promote the widespread application of autonomous vehicles.