The PIP-Net framework introduces a novel approach to predict pedestrian crossing intentions by Autonomous Vehicles (AVs) in real-world urban scenarios. By combining kinematic data and spatial features, the model outperforms existing methods, offering insights into scene dynamics and contextual perception. The Urban-PIP dataset is introduced for comprehensive pedestrian intention prediction studies, showcasing advancements in predicting crossing intentions up to 4 seconds in advance. The model's effectiveness is demonstrated through experiments on the PIE dataset, achieving state-of-the-art performance in accuracy and recall rates.
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