03 machine learning models - VforVitorio/F1_Strat_Manager GitHub Wiki

Machine Learning Models

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Machine Learning Models

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Overview of Machine Learning Components

The F1 Strategy Manager leverages multiple specialized machine learning models to handle different aspects of race strategy prediction:

  1. Lap Time Prediction (XGBoost) - Forecasts expected lap times based on numerous race factors
  2. Tire Degradation Modeling (TCN) - Predicts tire performance decline over race stints
  3. Vision-based Gap Calculation (YOLOv8) - Uses computer vision to identify cars and calculate gaps These models work together to feed the expert system with the predictions needed to generate optimal race strategies.

Model Architecture and Data Flow

The machine learning subsystem follows a multi-stage pipeline pattern, where raw data is processed through feature engineering steps before being fed into specialized models. The outputs are then standardized for consumption by the expert system.

Lap Time Prediction Model

The lap time prediction component uses XGBoost to forecast lap times with high accuracy (MAE = 0.09s and RMSE = 0.15s). It takes into account multiple factors that influence lap performance including tire compound, tire age, fuel load, and track conditions.

Model Features and Implementation

The XGBoost model processes a rich set of features derived from race telemetry:

Feature Category Examples Notes
Driver/Team DriverNumber, TeamID Captures team-specific performance
Tire CompoundID, TyreAge Critical for performance understanding
Speed SpeedI1, SpeedI2, SpeedFL, SpeedST Speed at different track sectors
Sequential Prev_LapTime, LapTime_Delta, LapTime_Trend Captures performance trends
Race Context Position, FuelLoad, DRSUsed Situational race factors
The model handles sequential data by creating derived features that track changes between laps and performance trends:

Usage and Integration

The lap time prediction model is exposed through a central predict_lap_times() function that handles the entire pipeline:

  1. Loading the trained model
  2. Validating input telemetry data
  3. Engineering sequential features
  4. Making predictions
  5. Formatting results for downstream use The function serves as the main interface between raw telemetry data and the expert system, providing both historical lap time analysis and future lap time forecasts.

Tire Degradation Model

The tire degradation component uses Temporal Convolutional Networks (TCN) to model how tire performance decreases over time. The model captures the non-linear nature of tire degradation for different compounds.

Degradation Metrics and Analysis

The system calculates several key degradation metrics from raw lap time data:

Metric Description Usage
TireDegAbsolute Raw lap time increase from baseline Direct performance loss
TireDegPercent Percentage lap time increase Relative performance change
FuelAdjustedDegAbsolute Degradation with fuel effect removed Isolates tire effects
DegradationRate Lap-to-lap change in performance Rate of performance loss
These metrics are calculated using functions like calculate_fuel_adjusted_metrics() and calculate_degradation_rate().

TCN Architecture and Implementation

The Temporal Convolutional Network is specifically designed to handle sequence modeling problems. For tire degradation, it:

  1. Takes a window of previous lap performance data (typically 5 laps)
  2. Processes through convolutional layers with dilated filters
  3. Outputs predictions for future degradation (next 3-5 laps) This approach captures how tire performance evolves over time, allowing for more accurate pit stop planning.

Vision-based Gap Calculation

The YOLOv8 computer vision model is used to identify teams from race footage, enabling gap calculation when telemetry data is unavailable or needs confirmation.

YOLOv8 Implementation

The system uses YOLOv8, a state-of-the-art object detection model that achieves over 90% mAP50 on team identification tasks. This model:

  1. Takes video frames as input
  2. Detects F1 cars in the frame
  3. Identifies the team/driver through livery recognition
  4. Calculates spatial relationships between detected cars This provides an independent source of gap data that complements telemetry-based calculations.

Integration with Expert System

The machine learning models are integrated with the expert system through a fact-based architecture. Each model outputs predictions that are converted into fact objects:

These facts trigger rules in the expert system, which then generates strategic recommendations based on the combined insights from all models.

Performance and Accuracy Metrics

The machine learning models are evaluated on various metrics to ensure reliable strategy recommendations:

Model Key Metrics Performance
XGBoost Lap Time Prediction MAE, RMSE MAE = 0.09s, RMSE = 0.15s
TCN Tire Degradation Degradation Rate Accuracy Within ±0.05s/lap
YOLOv8 Team Detection mAP50 >90%
These metrics guide ongoing model improvements and help users understand the confidence level of strategy recommendations.

Future Model Enhancements

The machine learning subsystem is designed for extensibility, with plans for:

  1. Enhanced weather impact modeling
  2. Driver-specific performance modeling
  3. Circuit-specific optimization models
  4. Expanded vision-based analytics These enhancements will continue to improve the accuracy and scope of the strategy recommendations.

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