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Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor

Mehmet Bilban1, Onur İnan2

  • 1Department of Computer Technologies, Necmettin Erbakan University, Konya 42370, Turkey.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Chaotic AdaBoost (CAB), an AI model for autonomous vehicles, enhancing sensor fusion accuracy to 99.3% for improved safety and reliability. CAB outperforms other methods in speed and acceleration prediction.

Keywords:
AdaBoostApache KafkaArtificial Neural NetworksChaotic AdaBoostGradient BoostingRandom Forestautonomous vehiclesk-Nearest Neighborsmachine learning

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Autonomous Vehicle Systems
  • Data Fusion

Background:

  • Real-time sensor fusion is critical for autonomous vehicle (AV) safety and performance.
  • Existing ensemble methods face limitations in handling sensor failures and dynamic driving conditions.
  • Robust data processing architectures are needed to manage high-volume sensor data streams.

Purpose of the Study:

  • To develop a novel AI-driven architecture for real-time sensor fusion in AVs.
  • To introduce Chaotic AdaBoost (CAB), an enhanced ensemble method for improved prediction accuracy and resilience.
  • To evaluate CAB's performance against other machine learning models in speed and acceleration prediction tasks.

Main Methods:

  • Developed a real-time sensor fusion architecture using Apache Kafka and MongoDB for data processing.
  • Introduced Chaotic AdaBoost (CAB), integrating a logistic chaotic map into the AdaBoost algorithm for enhanced weight updates.
  • Evaluated CAB, k-Nearest Neighbors (kNNs), Artificial Neural Networks (ANNs), standard AdaBoost (AB), Gradient Boosting (GBa), and Random Forest (RF) using the CARLA simulator.

Main Results:

  • CAB achieved 99.3% accuracy in speed and acceleration prediction, outperforming all compared methods.
  • CAB demonstrated superior performance with a Mean Squared Error (MSE) of 0.010 for speed and 0.018 for acceleration.
  • CAB achieved a mean Time-To-Collision (TTC) of 3.2 s and jerk of 0.15 m/s³, significantly improving upon standard AdaBoost.

Conclusions:

  • Chaotic AdaBoost (CAB) offers a robust and adaptable solution for real-time sensor fusion in autonomous vehicles.
  • The proposed architecture enhances resilience against sensor failures and dynamic conditions, crucial for AV safety.
  • CAB's performance advancements provide a scalable framework for improving operational reliability and passenger experience in autonomous driving.