Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty
This work addresses robustness in pedestrian trajectory forecasting for autonomous systems, but it is incremental as it builds on existing deep ensemble and uncertainty estimation techniques.
The paper tackled the problem of robust pedestrian trajectory forecasting under sensing uncertainty by proposing an end-to-end deep ensemble model that accounts for both perception and predictive uncertainty, resulting in more robust predictions and increased estimation accuracy compared to other Bayesian methods.
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.