Vehicle Control AI Trained on Driver Interaction Patterns

The patent applies copying by training the AI system on data captured from human operator interactions with the vehicle control interface. The system records and analyzes actual human driving behaviors, decisions, and responses to various situations, then uses this data to train neural networks that replicate human-like control patterns. This allows the automated system to copy and mimic human operator skills rather than relying on pre-programmed rules, thereby improving adaptability while maintaining automation.

The patent applies segmentation by dividing the complex AI system into distinct functional modules: an operator data collection module that captures human interaction data, a vehicle data collection module that records vehicle responses, an environment data collection module that tracks contextual information, and separate neural network components for different aspects of vehicle control. This modular segmentation manages system complexity by organizing functions into independent, trainable units while still achieving comprehensive vehicle control optimization through their integrated operation.

The patent applies preliminary action by continuously capturing and storing operator interaction data, vehicle response data, and environmental data during normal vehicle operation. This data is accumulated and prepared in advance for training the neural networks. By performing this data collection and preliminary processing during routine vehicle use rather than during dedicated training sessions, the system prepares training materials in advance, reducing the time required for actual model training while achieving high control precision through comprehensive pre-collected data.