Robust Neural Network for Sim-to-Real Gap in End-to-End Autonomous Driving

Stephan Pareigis, Fynn Maaß

2022

Abstract

A neural network architecture for end-to-end autonomous driving is presented, which is robust against discrepancies in system dynamics during the training process and in application. The proposed network architecture presents a first step to alleviate the simulation to reality gap with respect to differences in system dynamics. A vehicle is trained to drive inside a given lane in the CARLA simulator. The data is used to train NVIDIA’s PilotNet. When an offset is given to the steering angle of the vehicle while the trained network is being applied, PilotNet will not keep the vehicle inside the lane as expected. A new architecture is proposed called PilotNet∆, which is robust against steering angle offsets. Experiments in the simulator show that the vehicle will stay in the lane, although the steering properties of the vehicle differ

Paper Citation

in Harvard Style

Pareigis S. and Maaß F. (2022). Robust Neural Network for Sim-to-Real Gap in End-to-End Autonomous Driving. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-585-2, pages 113-119. DOI: 10.5220/0011140800003271

in Bibtex Style

@conference{icinco22,
author={Stephan Pareigis and Fynn Maaß},
title={Robust Neural Network for Sim-to-Real Gap in End-to-End Autonomous Driving},
booktitle={Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2022},
pages={113-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011140800003271},
isbn={978-989-758-585-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Robust Neural Network for Sim-to-Real Gap in End-to-End Autonomous Driving
SN - 978-989-758-585-2
AU - Pareigis S.
AU - Maaß F.
PY - 2022
SP - 113
EP - 119
DO - 10.5220/0011140800003271