论文标题
Banet：通过边界意识网络进行运动预测
BANet: Motion Forecasting with Boundary Aware Network
论文作者
论文摘要
我们提出了一个称为Banet的运动预测模型，该模型意味着边界感知网络，它是LaneGCN的变体。我们认为，仅使用车道中心线作为输入来获取向量图节点的嵌入功能是不够的。车道中心线只能提供车道的拓扑，矢量图的其他元素还包含丰富的信息。例如，车道边界可以提供流量规则约束信息，例如是否可以更改车道，这非常重要。因此，我们通过在运动预测模型中编码更多的向量图元素来实现更好的性能。我们在2022年Agroverse2 Motion预测挑战中报告结果，并在测试排行榜上排名第一。
We propose a motion forecasting model called BANet, which means Boundary-Aware Network, and it is a variant of LaneGCN. We believe that it is not enough to use only the lane centerline as input to obtain the embedding features of the vector map nodes. The lane centerline can only provide the topology of the lanes, and other elements of the vector map also contain rich information. For example, the lane boundary can provide traffic rule constraint information such as whether it is possible to change lanes which is very important. Therefore, we achieved better performance by encoding more vector map elements in the motion forecasting model.We report our results on the 2022 Argoverse2 Motion Forecasting challenge and rank 1st on the test leaderboard.
