IoT Data Fusion Techniques for Enhanced Situation Awareness in Autonomous Vehicle Networks: Explores IoT data fusion techniques to improve situation awareness within autonomous vehicle networks
Keywords:
IoT, Sensors, Machine LearningAbstract
The integration of Internet of Things (IoT) technologies in autonomous vehicles (AVs) has revolutionized the transportation industry, enabling advanced functionalities such as real-time data collection and analysis. One critical aspect in AV operation is situation awareness, which refers to the ability to perceive and understand environmental factors to make informed decisions. This paper explores IoT data fusion techniques to enhance situation awareness within AV networks. We discuss the challenges and opportunities of integrating IoT data sources, such as sensors, cameras, and vehicle-to-everything (V2X) communication, and propose a framework for efficient data fusion. The proposed framework leverages machine learning algorithms to process and integrate heterogeneous IoT data streams, enabling AVs to have a comprehensive understanding of their surroundings. Through case studies and simulations, we demonstrate the effectiveness of our approach in improving situation awareness, thereby enhancing the safety and efficiency of autonomous driving.
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