通过目标分类提升轨迹预测跨数据集泛化能力
Goal-based Trajectory Prediction for improved Cross-Dataset Generalization
- 构建异构图模型，融合车辆与道路网络信息
- 跨数据集测试中在NuScenes上表现优于基线模型
- 适合需要强泛化能力的自动驾驶场景
实现完全自动驾驶需准确理解周围环境，尤其对交通参与者未来状态的预测仍具挑战。当前最先进模型在真实数据集（如Argoverse2、NuScenes）上表现良好，但在新区域部署时性能显著下降，表明其泛化能力不足。本文提出一种新型图神经网络（GNN），利用包含交通参与者与矢量化道路网络的异构图，分阶段对轨迹终点（即目标）进行分类，从而提升对未见场景的泛化能力。通过跨数据集评估验证有效性：在Argoverse2上训练，在NuScenes上测试。
原文摘要 · Abstract (English)
To achieve full autonomous driving, a good understanding of the surrounding environment is necessary. Especially predicting the future states of other traffic participants imposes a non-trivial challenge. Current SotA-models already show promising results when trained on real datasets (e.g. Argoverse2, NuScenes). Problems arise when these models are deployed to new/unseen areas. Typically, performance drops significantly, indicating that the models lack generalization. In this work, we introduce a new Graph Neural Network (GNN) that utilizes a heterogeneous graph consisting of traffic participants and vectorized road network. Latter, is used to classify goals, i.e. endpoints of the predicted trajectories, in a multi-staged approach, leading to a better generalization to unseen scenarios. We show the effectiveness of the goal selection process via cross-dataset evaluation, i.e. training on Argoverse2 and evaluating on NuScenes.
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