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Unmanned Car Path Tracking Control Method Based on Machine Learning

The research of driverless cars is human-centered, hoping to create a safe, comfortable, convenient, and environmentally friendly transportation environment. However, in practical applications, it is found that due to changes in external conditions such as road factors, the vehicle's motion state deviates from the initial set value or cannot be tracked to the target position in real time. Therefore, there is an urgent need for a method to track and control driverless cars. This article studies methods from machine learning, with the purpose of further improving driverless technology through artificial intelligence. This article mainly uses the experimental method and the comparative method to compare and analyze the two unmanned vehicle path tracking control experiments. The experimental results show that the distance errors are different, and the minimum error is 0.062. This error proves that comparing the tracking error is beneficial to distance prediction and driving control.