End-to-end Driving via Conditional Imitation Learning
This addresses the limitation of uncontrollable autonomous driving systems for real-world navigation applications, though it is incremental as it builds on existing imitation learning methods.
The paper tackles the problem of imitation learning-based driving policies lacking test-time control, such as taking specific turns at intersections, by proposing conditional imitation learning that responds to high-level navigational commands. The result is a system that drives based on visual input in simulations and on a robotic truck, remaining responsive to commands.
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 https://youtu.be/cFtnflNe5fM