论文标题
Graph-SIM：基于图的时空相互作用建模，用于行人行动预测
Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction
论文作者
论文摘要
城市环境中自动驾驶汽车最关键但最具挑战性的任务之一是预测附近行人的未来行为，尤其是在过境点。预测行为取决于许多社会和环境因素，尤其是道路使用者之间的相互作用。捕获此类互动需要在三维空间中对道路使用者的场景和动态的全球视野。但是，当前的行人行为基准数据集缺少此信息。在这些挑战的激励下，我们提出了1）一种基于图形的新型模型，用于预测行人穿越行动。我们的方法模型，通过使用从鸟类视图中获得的功能，通过聚类和相对的交互加权，与附近的道路使用者的互动与附近的道路使用者的互动。 2）我们介绍了一个新数据集，该数据集为现有Nuscenes数据集提供3D边界框和行人行为注释。在新数据上，与现有方法相比，我们的方法通过提高各种指标的15％以上来实现最先进的性能。该数据集可从https://github.com/huawei-noah/datasets/pepscenes获得。
One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossing. Predicting behaviour depends on many social and environmental factors, particularly interactions between road users. Capturing such interactions requires a global view of the scene and dynamics of the road users in three-dimensional space. This information, however, is missing from the current pedestrian behaviour benchmark datasets. Motivated by these challenges, we propose 1) a novel graph-based model for predicting pedestrian crossing action. Our method models pedestrians' interactions with nearby road users through clustering and relative importance weighting of interactions using features obtained from the bird's-eye-view. 2) We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset. On the new data, our approach achieves state-of-the-art performance by improving on various metrics by more than 15% in comparison to existing methods. The dataset is available at https://github.com/huawei-noah/datasets/PePScenes.
