Design and Implementation of Intelligent In-vehicle Visual Recognition Interaction System by Fusion of Convolutional Neural Network (CNN) and Reinforcement Learning

This paper explores the integration of Convolutional Neural Networks (CNN) and Reinforcement Learning for in-vehicle visual recognition systems. Firstly, the importance of an in-vehicle visual recognition system is introduced, as well as the basic principles and potential of combining CNN and reinforcement learning. Then, the overall architecture of the system design and the selection of key technologies are elaborated in detail, including the CNN model suitable for in-vehicle environments, the selection basis of reinforcement learning algorithms, and the fusion strategy of the two. Through experimental setup and result analysis, the performance of the system in different scenarios is demonstrated and compared with existing methods, while performance bottlenecks and improvement suggestions are pointed out. Finally, the article explores the potential of the system in real-world application scenarios such as intelligent assisted driving and autonomous driving decision support and provides an outlook on future research directions.