  Uncertainty plays a key role in future prediction. The future is uncertain.
That means there might be many possible futures. A future prediction method
should cover the whole possibilities to be robust. In autonomous driving,
covering multiple modes in the prediction part is crucially important to make
safety-critical decisions. Although computer vision systems have advanced
tremendously in recent years, future prediction remains difficult today.
Several examples are uncertainty of the future, the requirement of full scene
understanding, and the noisy outputs space. In this thesis, we propose
solutions to these challenges by modeling the motion explicitly in a stochastic
way and learning the temporal dynamics in a latent space.
