Vehicle Destination Prediction Using Trip History Patterns

The system automatically collects trip history data and performs destination predictions without requiring operator intervention. The gradient boosted trees model self-trains on accumulated trip data, automatically improving prediction accuracy over time while reducing the operator's input burden to simple location confirmation.

The system pre-collects trip history data during normal vehicle operation and pre-trains the prediction model before actual destination prediction is needed. This preliminary data collection and model training enables fast, accurate predictions when the operator needs navigation assistance.

The patent replaces manual destination input mechanisms with an automated machine learning-based prediction system. The gradient boosted trees model processes trip history data algorithmically, substituting the mechanical interaction of manual entry with intelligent automated prediction.

The system transforms raw trip history data into meaningful prediction parameters by extracting features such as origin-destination pairs, time patterns, and frequency metrics. The gradient boosted trees model processes these transformed parameters to generate accurate destination predictions.

The system performs model training and data processing in advance, accumulating trip history data during normal vehicle operation. The gradient boosted trees model is trained offline on collected data, so when prediction is needed, the system can quickly query pre-trained models without extensive real-time processing.

The patent segments the data processing into distinct phases: data collection during trips, offline model training on accumulated data, and rapid prediction querying. This segmentation allows computationally intensive operations to occur when not time-critical, improving real-time performance.