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Autonomous vehicles navigate in dynamically changing environments under awide variety of conditions, being continuously influenced by surroundingobjects. Modelling interactions among agents is essential for accuratelyforecasting other agents' behaviour and achieving safe and comfortable motionplanning. In this work, we propose SCOUT, a novel Attention-based Graph NeuralNetwork that uses a flexible and generic representation of the scene as a graphfor modelling interactions, and predicts socially-consistent trajectories ofvehicles and Vulnerable Road Users (VRUs) under mixed traffic conditions. Weexplore three different attention mechanisms and test our scheme with bothbird-eye-view and on-vehicle urban data, achieving superior performance thanexisting state-of-the-art approaches on InD and ApolloScape Trajectorybenchmarks. Additionally, we evaluate our model's flexibility andtransferability by testing it under completely new scenarios on RounD dataset.The importance and influence of each interaction in the final prediction isexplored by means of Integrated Gradients technique and the visualization ofthe attention learned.

SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs