DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a…

• MU
  Multidisciplinary University Research Initiative
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/N019474/1, EP/M013774/1, EP/I001107/2