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This paper considers semantic forecasting in road-driving scenes. Mostexisting approaches address this problem as deterministic regression of futurefeatures or future predictions given observed frames. However, such approachesignore the fact that future can not always be guessed with certainty. Forexample, when a car is about to turn around a corner, the road which iscurrently occluded by buildings may turn out to be either free to drive, oroccupied by people, other vehicles or roadworks. When a deterministic modelconfronts such situation, its best guess is to forecast the most likelyoutcome. However, this is not acceptable since it defeats the purpose offorecasting to improve security. It also throws away valuable training data,since a deterministic model is unable to learn any deviation from the norm. Weaddress this problem by providing more freedom to the model through allowing itto forecast different futures. We propose to formulate multimodal forecastingas sampling of a multimodal generative model conditioned on the observedframes. Experiments on the Cityscapes dataset reveal that our multimodal modeloutperforms its deterministic counterpart in short-term forecasting whileperforming slightly worse in the mid-term case.

Multimodal semantic forecasting based on conditional generation of future features