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Ensemble Learning Framework for Anomaly Detection in Autonomous Driving Systems.

Sazid Nazat1, Walaa Alayed2, Lingxi Li1

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Summary
This summary is machine-generated.

Ensemble learning significantly improves anomaly detection in autonomous driving systems. These advanced models outperform individual AI, enhancing safety and reliability by reducing false positives.

Keywords:
VANET securityanomaly detectionautonomous driving systemsdata engineeringensemble learningmachine learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Autonomous Systems

Background:

  • Individual AI models have inherent limitations for anomaly detection.
  • Securing autonomous driving systems requires robust anomaly detection techniques.

Purpose of the Study:

  • To propose and assess an ensemble learning framework for anomaly detection in autonomous driving.
  • To evaluate the effectiveness of ensemble models against individual models using VeReMi and Sensor datasets.

Main Methods:

  • Rigorous evaluation of ensemble learning models against individual models.
  • Binary and multiclass classification tasks were performed on autonomous vehicle datasets.
  • Performance metrics included accuracy, precision, recall, false positive rates, and F1-score.

Main Results:

  • Ensemble models consistently outperformed individual models across all evaluated metrics.
  • On the VeReMi dataset, ensembles achieved a maximum accuracy of 0.80 and F1-score of 0.86.
  • On the Sensor dataset, ensemble models like CatBoost achieved perfect accuracy, precision, recall, and F1-score.
  • Ensemble methods reduced false positives, significantly enhancing system reliability.

Conclusions:

  • Ensemble learning provides a robust solution for anomaly detection in autonomous driving.
  • The proposed framework enhances the accuracy and reliability of autonomous driving systems.
  • Despite increased runtime, ensemble models offer superior performance for critical safety applications.