@InProceedings{FelipeCodevilla2018,
author="Felipe Codevilla
and Matthias Muller
and Antonio Lopez
and Vladlen Koltun
and Alexey Dosovitskiy",
title="End-to-end Driving via Conditional Imitation Learning",
booktitle="IEEE International Conference on Robotics and Automation",
year="2018",
pages="4693--4700",
abstract="Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL",
optnote="ADAS; 600.116; 600.124; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3108), last updated on Mon, 24 Jan 2022 12:31:53 +0100",
doi="10.1109/ICRA.2018.8460487",
file=":http://refbase.cvc.uab.es/files/CML2018.pdf:PDF"
}