Model-Based Imitation Learning for Urban Driving

Anthony Hu · Gianluca Corrado · Nicolas Griffiths · Zachary Murez · Corina Gurau · Hudson Yeo · Alex Kendall · Roberto Cipolla · Jamie Shotton

Hall J #521

Keywords: [ World Models ] [ imitation learning ] [ autonomous driving ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Thu 1 Dec 2 p.m. PST — 4 p.m. PST


An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomous driving. Our method leverages 3D geometry as an inductive bias and learns a highly compact latent space directly from high-resolution videos of expert demonstrations. Our model is trained on an offline corpus of urban driving data, without any online interaction with the environment. MILE improves upon prior state-of-the-art by 31% in driving score on the CARLA simulator when deployed in a completely new town and new weather conditions. Our model can predict diverse and plausible states and actions, that can be interpretably decoded to bird's-eye view semantic segmentation. Further, we demonstrate that it can execute complex driving manoeuvres from plans entirely predicted in imagination. Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment. The code and model weights are available at

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