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<p>This study investigates the critical control factors differentiating human-driven vehicles from IoT edge-enabled smart driving systems Real-time steering, throttle, and brake control are the main areas of emphasis. By combining many high-precision sensors and using edge computing for real-time processing, the research seeks to improve autonomous vehicle decision-making. The suggested system gathers real-time time-series data using LiDAR, radar, GPS, IMU, and ultrasonic sensors. Before sending this data to a cloud server, edge nodes preprocess it. There, a Convolutional Neural Network (CNN) creates predicted control vectors for vehicle navigation. The study uses a MATLAB 2023 simulation framework that includes 100 autonomous cars, five edge nodes, and a centralized cloud server. Multiple convolutional and pooling layers make up the CNN architecture, which is followed by fully linked layers. To enhance trajectory estimation, grayscale and optical flow pictures are used. Trajectory smoothness measures, loss function trends, and Root Mean Square Error (RMSE) are used to evaluate performance. According to experimental data, the suggested CNN-based edge-enabled driving system outperforms conventional autonomous driving techniques in terms of navigation accuracy, achieving an RMSE of 15.123 and a loss value of 2.114. The results show how edge computing may improve vehicle autonomy and reduce computational delay, opening the door for more effective smart driving systems. In order to better evaluate the system’s suitability for dynamic situations, future study will incorporate real-world validation.</p>

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Enhanced CNN based approach for IoT edge enabled smart car driving system for improving real time control and navigation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Majjari Sudhakar,
  • K. Vivekrabinson

摘要

This study investigates the critical control factors differentiating human-driven vehicles from IoT edge-enabled smart driving systems Real-time steering, throttle, and brake control are the main areas of emphasis. By combining many high-precision sensors and using edge computing for real-time processing, the research seeks to improve autonomous vehicle decision-making. The suggested system gathers real-time time-series data using LiDAR, radar, GPS, IMU, and ultrasonic sensors. Before sending this data to a cloud server, edge nodes preprocess it. There, a Convolutional Neural Network (CNN) creates predicted control vectors for vehicle navigation. The study uses a MATLAB 2023 simulation framework that includes 100 autonomous cars, five edge nodes, and a centralized cloud server. Multiple convolutional and pooling layers make up the CNN architecture, which is followed by fully linked layers. To enhance trajectory estimation, grayscale and optical flow pictures are used. Trajectory smoothness measures, loss function trends, and Root Mean Square Error (RMSE) are used to evaluate performance. According to experimental data, the suggested CNN-based edge-enabled driving system outperforms conventional autonomous driving techniques in terms of navigation accuracy, achieving an RMSE of 15.123 and a loss value of 2.114. The results show how edge computing may improve vehicle autonomy and reduce computational delay, opening the door for more effective smart driving systems. In order to better evaluate the system’s suitability for dynamic situations, future study will incorporate real-world validation.