SCOUT：社会协调和UndersTandable图形注意力网络在车辆和VRU轨迹预测中的应用
SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs
摘要：
自动驾驶车辆在各种条件下在动态变化的环境中导航，不断受到周围物体的影响。智能体之间的交互建模对于准确预测其他智能体的行为和实现安全舒适的运动规划至关重要。在这项工作中，我们提出了SCOUT，一种新的基于注意力的图神经网络，使用灵活和通用的表示场景作为一个图形建模的互动，并预测社会一致的轨迹车辆和弱势道路使用者（VRU）在混合交通条件下。我们探索三种不同的注意力机制和测试我们的计划与鸟瞰图和车载城市数据，实现优于现有的国家的最先进的方法在InD和ApolloScape轨迹基准。此外，我们通过在RoundD数据集上的全新场景下对其进行测试来评估我们模型的灵活性和可移植性。通过整合注意力技术和注意力学习的可视化，探讨了每种交互在最终预测中的重要性和影响。
英文摘要：
Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects. Modelling interactions among agents is essential for accurately forecasting other agents' behaviour and achieving safe and comfortable motion planning. In this work, we propose SCOUT, a novel Attention-based Graph Neural Network that uses a flexible and generic representation of the scene as a graph for modelling interactions, and predicts socially-consistent trajectories of vehicles and Vulnerable Road Users (VRUs) under mixed traffic conditions. We explore three different attention mechanisms and test our scheme with both bird-eye-view and on-vehicle urban data, achieving superior performance than existing state-of-the-art approaches on InD and ApolloScape Trajectory benchmarks. Additionally, we evaluate our model's flexibility and transferability by testing it under completely new scenarios on RounD dataset. The importance and influence of each interaction in the final prediction is explored by means of Integrated Gradients technique and the visualization of the attention learned.
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