  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.

Machine Learning;Artificial Intelligence