End-to-end Driving via Conditional Imitation Learning

Code

• github.com/carla-simulator/imitation-learning
  Officialtf★ 0
• github.com/shin-eunsu/GTA_Autodriving_LogitechG29
  none★ 0
• github.com/shunchan0677/deepware
  tf★ 0
• github.com/bitsauce/Carla-ppo
  tf★ 0
• github.com/Suryavf/SelfDrivingCar
  pytorch★ 0
• github.com/onlytailei/carla_cil_pytorch
  pytorch★ 0
• github.com/acm-uiuc/racerbot.rl
  pytorch★ 0

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 https://youtu.be/cFtnflNe5fM

Tasks

Reproductions