Real-Time Traffic Sign Decoding with Advanced Sensor Fusion and Deep Learning
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Abstract
Accurate and real-time traffic sign recognition is crucial for safe navigation and autonomous driving. However, traditional camera-based systems struggle with varying lighting, occlusions, and adverse weather. This research addresses this by proposing a novel approach for real-time traffic sign decoding that leverages advanced sensor fusion and deep learning. The framework integrates data from cameras, LiDAR (3D environment information), and potentially radar (effective in low-light) to create a robust representation. This richer data is then processed by a deep learning model specifically designed for traffic sign recognition. We evaluate the framework on real-world datasets captured in diverse driving environments. Performance metrics focus on real-time accuracy of traffic sign decoding. The results demonstrate that the proposed approach achieves high accuracy, significantly enhancing the reliability and robustness of traffic sign recognition systems. This research, applicable to various autonomous driving and navigation applications, paves the way for safer and more efficient on-road experiences.
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