Real-time vehicle control via edge cloud sensor fusion and CNN based perceptron.

Reliable real-time vehicle control is essential for intelligent transport systems where accurate perception and decision-making depend on fast sensor data processing. This study developed a hybrid edge-cloud method integrating deep learning with Internet of Things (IoT) sensor fusion for adaptive vehicle control. Ultrasonic range data were combined with convolutional neural networks (CNNs) to enable object detection, stopping-time prediction, and braking control under varying environmental conditions. The CNN-based model was trained and evaluated under normal and simulated adverse driving scenarios. Results indicated strong performance with R² = 0.99 under normal and 0.98 under adverse conditions, and a mean squared error (MSE) of 0.0085. Average inference latency is 110-116 ms on Jetson Nano and 210-230 ms on Raspberry Pi, confirming suitability for real-time deployment on edge hardware. The hybrid edge-cloud method enables adaptive, real-time vehicle control through IoT sensor fusion. CNN-based perception enhances prediction accuracy and operational safety under variable driving conditions. Demonstrates feasibility of deep learning deployment on low-cost edge devices for intelligent transport applications. Thus, integrating deep learning with IoT-enabled sensors on an edge-cloud platform provides a reliable and scalable pathway toward safe, adaptive, and efficient vehicle control in intelligent transportation systems.