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.

翻译：准确预测轨迹对于各类系统（如高级驾驶辅助系统和自动驾驶车辆）的性能至关重要。这些预测有助于预判可能导致碰撞的事件，从而有效规避风险。深度神经网络在运动预测领域表现卓越，但仍存在过度自信和不确定性量化等问题。深度集成方法虽能缓解上述问题，但在多模态分布中的应用仍具挑战。本文提出了一种名为分层轻量Transformer集成（HLT-Ens）的新方法，旨在利用新型分层损失函数高效训练Transformer架构集成模型。HLT-Ens借鉴分组卷积技术，通过分组全连接层有效捕获多模态分布。通过大量实验证明，HLT-Ens达到了最先进的性能水平，为改进轨迹预测技术提供了富有前景的途径。