  The feasibility of collecting a large amount of expert demonstrations has
inspired growing research interests in learning-to-drive settings, where models
learn by imitating the driving behaviour from experts. However, exclusively
relying on imitation can limit agents' generalisability to novel scenarios that
are outside the support of the training data. In this paper, we address this
challenge by factorising the driving task, based on the intuition that modular
architectures are more generalisable and more robust to changes in the
environment compared to monolithic, end-to-end frameworks. Specifically, we
draw inspiration from the trajectory forecasting community and reformulate the
learning-to-drive task as obstacle-aware perception and grounding,
distribution-aware goal prediction, and model-based planning. Firstly, we train
the obstacle-aware perception module to extract salient representation of the
visual context. Then, we learn a multi-modal goal distribution by performing
conditional density-estimation using normalising flow. Finally, we ground
candidate trajectory predictions road geometry, and plan the actions based on
on vehicle dynamics. Under the CARLA simulator, we report state-of-the-art
results on the CARNOVEL benchmark.
