SoPhie
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Overview
This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model physical and social interactions. Our approach blends asocial attention mechanism with a physical attention that helps the model to learn where to look in a large scene and extract the most salient parts of the image relevant to the path.
Requirements
- OpenCV==4.1.1
- YAML
- ProDict
- torch (1.8.0+cu111)
- torchfile (0.1.0)
- torchsummary (1.5.1)
- torchtext (0.5.0)
- torchvision (0.9.0+cu111)
Get Started and Usage
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Quantitative results
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Qualitative results
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