CAR-Net: Clairvoyant Attentive Recurrent Network

Understand

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment.

• We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene.
• We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task.
• Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent.