Real-time vehicle control via edge cloud sensor fusion and CNN based perceptron
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hybrid edge-cloud system using deep learning and Internet of Things (IoT) sensors for adaptive vehicle control. The method ensures safe and efficient real-time driving, even in adverse conditions.
Area Of Science
- Intelligent Transportation Systems
- Edge Computing
- Deep Learning
Background
- Real-time vehicle control is critical for intelligent transport systems, relying on rapid sensor data processing for perception and decision-making.
- Current systems face challenges in adapting to varying environmental conditions and ensuring operational safety.
Purpose Of The Study
- To develop a hybrid edge-cloud method integrating deep learning with Internet of Things (IoT) sensor fusion for adaptive vehicle control.
- To enhance object detection, stopping-time prediction, and braking control under diverse driving scenarios.
Main Methods
- Utilized ultrasonic range data fused with convolutional neural networks (CNNs) for perception tasks.
- Implemented a hybrid edge-cloud architecture for processing and control.
- Trained and evaluated the CNN model under normal and simulated adverse driving conditions on edge hardware (Jetson Nano, Raspberry Pi).
Main Results
- Achieved high performance with R² = 0.99 (normal) and R² = 0.98 (adverse conditions).
- Recorded a low mean squared error (MSE) of 0.0085.
- Demonstrated low inference latency (110-230 ms) suitable for real-time edge deployment.
Conclusions
- The hybrid edge-cloud approach enables adaptive, real-time vehicle control through IoT sensor fusion and CNN-based perception.
- The system enhances prediction accuracy and operational safety in variable driving conditions.
- Confirms the feasibility of deploying deep learning on low-cost edge devices for intelligent transport applications.
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