Model-Based Imitation Learning for Urban Driving
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 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 https://github.com/wayveai/mile.
Cite
@article{arxiv.2210.07729,
title = {Model-Based Imitation Learning for Urban Driving},
author = {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},
journal= {arXiv preprint arXiv:2210.07729},
year = {2022}
}
Comments
NeurIPS 2022