Accurate prediction of the future trajectories of traffic agents is a critical aspect of autonomous vehicle navigation. However, most existing approaches focus on predicting trajectories from a static roadside perspective, ignoring the influence of autonomous vehicles’ future plans on neighboring traffic agents. To address this challenge, this paper introduces EPG-MGCN, an ego-planning-guided multi-graph convolutional network. EPG-MGCN leverages graph convolutional networks and ego-planning guidance to predict the trajectories of heterogeneous traffic agents near the ego vehicle. The model captures interactions through multiple graph topologies from four distinct perspectives: distance, visibility, ego planning, and category. Additionally, it encodes the ego vehicle's planning information via the planning graph and a planning-guided prediction module. The model is evaluated on three challenging trajectory datasets: ApolloScape, nuScenes, and next generation simulation (NGSIM). Comparative evaluations against mainstream methods demonstrate its superior predictive capabilities and inference speed.

中文翻译：

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用于异构智能体轨迹预测的自我规划引导多图卷积网络


准确预测交通人员的未来轨迹是自动车辆导航的一个关键方面。然而，大多数现有方法侧重于从静态路边角度预测轨迹，忽略了自动驾驶车辆未来计划对邻近交通代理的影响。为了应对这一挑战，本文引入了 EPG-MGCN，一种自我规划引导的多图卷积网络。 EPG-MGCN 利用图卷积网络和自我规划指导来预测自我车辆附近异构交通代理的轨迹。该模型从四个不同的角度通过多个图拓扑捕获交互：距离、可见性、自我规划和类别。此外，它还通过规划图和规划引导的预测模块对自我车辆的规划信息进行编码。该模型在三个具有挑战性的轨迹数据集上进行评估：ApolloScape、nuScenes 和下一代模拟 (NGSIM)。与主流方法的对比评估表明其优越的预测能力和推理速度。