TY - CONF AU - Felipe Codevilla AU - Matthias Muller AU - Antonio Lopez AU - Vladlen Koltun AU - Alexey Dosovitskiy A2 - ICRA PY - 2018// TI - End-to-end Driving via Conditional Imitation Learning BT - IEEE International Conference on Robotics and Automation SP - 4693 EP - 4700 N2 - 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 L1 - http://refbase.cvc.uab.es/files/CML2018.pdf UR - http://dx.doi.org/10.1109/ICRA.2018.8460487 N1 - ADAS; 600.116; 600.124; 600.118 ID - Felipe Codevilla2018 ER -