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.
翻译:在各种条件下,机动车辆在不断变化的环境中航行,并不断受到周围物体的影响。代理人之间的模拟互动对于准确预测其他代理人的行为以及实现安全和舒适的运动规划至关重要。在这项工作中,我们提议SCOUT,这是一个新的基于注意力的图形神经网络,它使用灵活和通用的场景描述图作为模拟互动的图表,并预测在混合交通条件下车辆和脆弱道路使用者的社会一致轨迹。我们探索三种不同的关注机制,并测试我们使用鸟眼和车辆城市数据的办法,取得优于现有最新的InD和ApoloScappe轨迹基准方法的业绩。此外,我们通过在全新情景下对RounD数据集进行测试,评估我们的模型的灵活性和可转移性。通过综合重力技术和对所了解的注意力进行可视化,来探索最终预测中每种互动的重要性和影响。