  We study the design of learning architectures for behavioural planning in a
dense traffic setting. Such architectures should deal with a varying number of
nearby vehicles, be invariant to the ordering chosen to describe them, while
staying accurate and compact. We observe that the two most popular
representations in the literature do not fit these criteria, and perform badly
on an complex negotiation task. We propose an attention-based architecture that
satisfies all these properties and explicitly accounts for the existing
interactions between the traffic participants. We show that this architecture
leads to significant performance gains, and is able to capture interactions
patterns that can be visualised and qualitatively interpreted. Videos and code
are available at https://eleurent.github.io/social-attention/.
