Abstract
Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving. However, most existing methods overlook complex interactions between traffic participants that often govern their future trajectories. In this paper, we propose SocialFormer, an agent interaction-aware trajectory prediction method that leverages the semantic relationship between the target vehicle and surrounding vehicles by making use of the road topology. We also introduce an edge-enhanced heterogeneous graph transformer (EHGT) as the aggregator in a graph neural network (GNN) to encode the semantic and spatial agent interaction information. Additionally, we introduce a temporal encoder based on gated recurrent units (GRU) to model the temporal social behavior of agent movements. Finally, we present an information fusion framework that integrates agent encoding, lane encoding, and agent interaction encoding for a holistic representation of the traffic scene. We evaluate SocialFormer for the trajectory prediction task on the popular nuScenes benchmark and achieve state-of-the-art performance.
Abstract (translated)
准确的轨迹预测对于确保安全和高效的自动驾驶至关重要。然而，大多数现有方法忽视了交通参与者之间的复杂交互，这些交互通常决定了他们的未来轨迹。在本文中，我们提出了SocialFormer，一种关注代理与周围车辆之间语义关系的轨迹预测方法，通过利用道路拓扑结构来利用目标车辆与周围车辆之间的语义关系。我们还引入了一个增强的异质图变换器（EHGT）作为图神经网络（GNN）的聚合器，以编码代理的语义和空间交互信息。此外，我们还引入了一个基于门控循环单元（GRU）的时间编码器来建模代理运动的时间社交行为。最后，我们提出了一个整合代理编码、路况编码和代理交互编码的信息融合框架，以对交通场景进行全面的表示。我们在流行的nuScenes基准上评估SocialFormer的轨迹预测任务，并取得了最先进的性能。
URL
https://arxiv.org/abs/2405.03809
