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

Authors Abstract
Content
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 the actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed the computational efficiency of the pipeline and evaluated the robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity.
Meta TagsDetails
DOI
https://doi.org/10.4271/12-04-03-0023
Pages
18
Citation
Samak, T., Samak, C., and Kandhasamy, S., "Robust Behavioral Cloning for Autonomous Vehicles Using End-to-End Imitation Learning," Connected and Automated Vehicles 4(3):279-295, 2021, https://doi.org/10.4271/12-04-03-0023.
Additional Details
Publisher
Published
Aug 19, 2021
Product Code
12-04-03-0023
Content Type
Journal Article
Language
English