Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning

Understand

In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning.

• The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle.
• The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neural network, following which, the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera.
• A novel coupled control law was formulated to generate longitudinal control commands on-the-go based on the predicted steering angle and other parameters such as actual speed of the ego vehicle and the prescribed constraints for speed and steering.