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

Understand

Predicting vulnerable road 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 network models for this task.
• However, these models are not robust and certain issues still need to be resolved.