Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor Fusion in Autonomous Vehicles: Enhancing Speed and Acceleration Prediction Under Uncertainty

Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor Fusion in Autonomous Vehicles: Enhancing Speed and Acceleration Prediction Under Uncertainty

This study presents a novel artificial intelligence-driven architecture for real-time sensor fusion in autonomous vehicles (AVs), leveraging Apache Kafka and MongoDB for synchronous and asynchronous data processing to enhance resilience against sensor failures and dynamic conditions. We introduce Ch...

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Bibliographic Details
Main Authors: Mehmet Bilban, Onur İnan
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
AdaBoost
Apache Kafka
Artificial Neural Networks
autonomous vehicles
Gradient Boosting
k-Nearest Neighbors
Online Access:https://www.mdpi.com/1424-8220/25/11/3485
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