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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/1424-8220/25/11/3485

Similar Items