SCIENCE@home

End-to-End Driving Via Conditional Imitation Learning

Felipe Codevilla,Matthias Müller,Alexey Dosovitskiy,Antonio M. López,V. Koltun

Published 2017 in IEEE International Conference on Robotics and Automation

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.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-38 of 38 references · Page 1 of 1

CITED BY

Showing 1-100 of 1208 citing papers · Page 1 of 13

123413
Next