Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting

arXiv:2403.17678v1 Announce Type: new
Abstract: Accurate trajectory forecasting is crucial for the performance of various systems, such as advanced driver-assistance systems and self-driving vehicles. These forecasts allow to anticipate events leading to collisions and, therefore, to mitigate them. Deep Neural Networks have excelled in motion forecasting, but issues like overconfidence and uncertainty quantification persist. Deep Ensembles address these concerns, yet applying them to multimodal distributions remains challenging. In this paper, we propose a novel approach named Hierarchical Light Transformer Ensembles …

abstract advanced arxiv cs.cv driver driving events forecasting hierarchical light multimodal networks neural networks performance self-driving self-driving vehicles systems them trajectory transformer type uncertainty vehicles