End-to-End Deep Conditional Imitation Learning for Autonomous Driving

Autonomous urban driving has a been rising problem since decades because of interactions with very complex environment. The traditional modular pipeline, rule-based algorithms, has not presented yet an efficient model to rely on, because it can not cover the very large possible scenarios space. Machine Learning techniques like supervised learning or imitation learning and Reinforcement learning have initial promising results with better performance. We propose an end-to-end Deep Conditional Imitation Learning model for autonomous driving inspired by both of Intel and Nvidia. The feature extraction part for Intel is replaced with Nvidia's model. Our proposed model outperforms Intel's architecture performance on CARLA Simulator, and overgeneralizes on various towns with different weather conditions.