Model-Based Imitation Learning for Urban Driving

• URL: http://arxiv.org/abs/2210.07729v1
• Date: Fri, 14 Oct 2022 11:59:46 GMT
• Title: Model-Based Imitation Learning for Urban Driving
• Authors: Anthony Hu and Gianluca Corrado and Nicolas Griffiths and Zak Murez and Corina Gurau and Hudson Yeo and Alex Kendall and Roberto Cipolla and Jamie Shotton
• Abstract summary: We present MILE: a Model-based Imitation LEarning approach to jointly learn a model of the world and a policy for autonomous driving. Our model is trained on an offline corpus of urban driving data, without any online interaction with the environment. Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.
• Score: 26.782783239210087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 35% 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 https://github.com/wayveai/mile.

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