  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 (HLT-Ens), aimed at efficiently
training an ensemble of Transformer architectures using a novel hierarchical
loss function. HLT-Ens leverages grouped fully connected layers, inspired by
grouped convolution techniques, to capture multimodal distributions,
effectively. Through extensive experimentation, we demonstrate that HLT-Ens
achieves state-of-the-art performance levels, offering a promising avenue for
improving trajectory forecasting techniques.
