Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio-Temporal Attention

Predicting vulnerableroad user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural...

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Veröffentlicht in:	IEEE transactions on intelligent vehicles 2022-06, Vol.7 (2), p.221-230
Hauptverfasser:	Yang, Dongfang, Zhang, Haolin, Yurtsever, Ekim, Redmill, Keith A., Ozguner, Umit
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Artificial neural networks autonomous driving Computer architecture Feature extraction Image segmentation Neural networks Pedestrian crossings Pedestrian intention Pedestrians Prediction algorithms Predictive models Recurrent neural networks Roads Semantics Source code spatial-temporal fusion Task analysis Traffic speed Visualization
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