  Imitation learning is a powerful approach for learning autonomous driving
policy by leveraging data from expert driver demonstrations. However, driving
policies trained via imitation learning that neglect the causal structure of
expert demonstrations yield two undesirable behaviors: inertia and collision.
In this paper, we propose Causal Imitative Model (CIM) to address inertia and
collision problems. CIM explicitly discovers the causal model and utilizes it
to train the policy. Specifically, CIM disentangles the input to a set of
latent variables, selects the causal variables, and determines the next
position by leveraging the selected variables. Our experiments show that our
method outperforms previous work in terms of inertia and collision rates.
Moreover, thanks to exploiting the causal structure, CIM shrinks the input
dimension to only two, hence, can adapt to new environments in a few-shot
setting. Code is available at https://github.com/vita-epfl/CIM.
