Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization

Understand

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.

• This problem can be formalized as a sequence prediction problem, where a number of observations are used to predict the sequence into the future.
• However, real-world scenarios demand a model of uncertainty of such predictions, as future states become increasingly uncertain and multi-modal -- in particular on long time horizons.
• This makes modelling and learning challenging.