Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing
  Uncertainty
Anshul Nayak Azim Eskandarian Zachary Doerzaph Prasenjit Ghorai
One of the fundamental challenges in the prediction of dynamic agents is
robustness. Usually, most predictions are deterministic estimates of future
states which are over-confident and prone to error. Recently, few works have
addressed capturing uncertainty during forecasting of future states. However,
these probabilistic estimation methods fail to account for the upstream noise
in perception data during tracking. Sensors always have noise and state
estimation becomes even more difficult under adverse weather conditions and
occlusion. Traditionally, Bayes filters have been used to fuse information from
noisy sensors to update states with associated belief. But, they fail to
address non-linearities and long-term predictions. Therefore, we propose an
end-to-end estimator that can take noisy sensor measurements and make robust
future state predictions with uncertainty bounds while simultaneously taking
into consideration the upstream perceptual uncertainty. For the current
research, we consider an encoder-decoder based deep ensemble network for
capturing both perception and predictive uncertainty simultaneously. We
compared the current model to other approximate Bayesian inference methods.
Overall, deep ensembles provided more robust predictions and the consideration
of upstream uncertainty further increased the estimation accuracy for the
model.