Research on Hybrid Intelligent Decision-Making and Pattern Recognition Methods in Autonomous Driving Systems

As the technology of autonomous driving develops with each passing day, the use of intelligent decision-making and pattern recognition methods in autonomous driving systems is becoming increasingly significant, making how to efficiently and accurately achieve decision-making and pattern recognition a hot topic of research in the field of autonomous driving. We counter that hybrid decision-making and pattern recognition can be achieved enhancing existing deep learning and reinforcement learning models. First, we build a multi-level perception system using Long Short-Term Memory Network (LSTM), which is capable of effectively abstracting spatial and temporal properties in the road environment. Next, an adaptive decision-making module is incorporated to dynamically optimize based on real-time traffic conditions and environmental changes. Then, the usage of fuzzy logic, which can describe the uncertainty of sensory information, and Bayesian network can also enhance the robustness and accuracy of the decision-making system, realizing the whole process of perception and decision-making. This paper verifies the efficacy of the proposed strategy in experiments on ApolloScape dataset and observes that the model achieves high accuracy and real-time performance in complex environments, and the performance has been greatly improved compared with previous single decision-making methods.