PT Unknown AU Felipe Codevilla Matthias Muller Antonio Lopez Vladlen Koltun Alexey Dosovitskiy TI End-to-end Driving via Conditional Imitation Learning BT IEEE International Conference on Robotics and Automation PY 2018 BP 4693 EP 4700 DI 10.1109/ICRA.2018.8460487 AB 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 ER