Research on the Application of Machine Learning in the Real Time Decision System of Autonomous Vehicles

Abstract

As human society enters the era of interconnection and intelligence; the rapid development of the automotive industry has made intelligent driving technology a new industry focus. This article conducts research on the application of machine learning in real-time decision-making systems for autonomous vehicles. The use of radial basis function artificial neural networks to solve the classification problem of overtaking intentions has been proven to be a universal approximator. Based on theoretical analysis and practical driving experience, it can be seen that driving behavior is often stimulated and influenced by multiple factors such as people, cars, roads, and the environment during the driving decision-making process. These factors are collectively referred to as driving decision influencing factors. Assist vehicle sensors in making decisions based on the identified signage information, accurately control the vehicle's driving route based on the identified lane markings, and improve the sensitivity of the sensors and the resilience of the entire system. Considering that the motor speed changes rapidly and in order to ensure stable transmission throughout the entire driving process, the system needs to be adjusted slowly, even if there is no small deviation adjustment, the adjustment speed is fast, and the large deviation generated during cornering or overtaking can be adjusted to prevent slow tracking response speed.