Edge AI for Real-Time Object Detection in Autonomous Vehicles
Abstract
Autonomous vehicles require low-latency object detection systems to ensure safe navigation in dynamic environments. This paper introduces an Edge AI framework that integrates lightweight deep learning models optimized for deployment on edge devices. By leveraging hardware acceleration and model quantization techniques, the framework achieves real-time object detection with minimal computational overhead. The system is evaluated on real-world autonomous driving datasets, demonstrating high accuracy and low inference latency. The study highlights the potential of Edge AI to enhance the responsiveness and reliability of autonomous vehicles, reducing dependency on centralized cloud systems.
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