Combining YOLO and Deep Reinforcement Learning for Autonomous Driving in Public Roadworks Scenarios

Nuno Andrade, Tiago Ribeiro, Joana Coelho, Gil Lopes, A. Ribeiro

2022

Abstract

Autonomous driving is emerging as a useful practical application of Artificial Intelligence (AI) algorithms regarding both supervised learning and reinforcement learning methods. AI is a well-known solution for some autonomous driving problems but it is not yet established and fully researched for facing real world problems regarding specific situations human drivers face every day, such as temporary roadworks and temporary signs. This is the core motivation for the proposed framework in this project. YOLOv3-tiny is used for detecting roadworks signs in the path traveled by the vehicle. Deep Deterministic Policy Gradient (DDPG) is used for controlling the behavior of the vehicle when overtaking the working zones. Security and safety of the passengers and the surrounding environment are the main concern taken into account. YOLOv3-tiny achieved an 94.8% mAP and proved to be reliable in real-world applications. DDPG made the vehicle behave with success more than 50% of the episodes when testing, although still needs some improvements to be transported to the real-world for secure and safe driving.

Paper Citation

in Harvard Style

Andrade N., Ribeiro T., Coelho J., Lopes G. and Ribeiro A. (2022). Combining YOLO and Deep Reinforcement Learning for Autonomous Driving in Public Roadworks Scenarios. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 793-800. DOI: 10.5220/0010913600003116

in Bibtex Style

@conference{icaart22,
author={Nuno Andrade and Tiago Ribeiro and Joana Coelho and Gil Lopes and A. Ribeiro},
title={Combining YOLO and Deep Reinforcement Learning for Autonomous Driving in Public Roadworks Scenarios},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={793-800},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010913600003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Combining YOLO and Deep Reinforcement Learning for Autonomous Driving in Public Roadworks Scenarios
SN - 978-989-758-547-0
AU - Andrade N.
AU - Ribeiro T.
AU - Coelho J.
AU - Lopes G.
AU - Ribeiro A.
PY - 2022
SP - 793
EP - 800
DO - 10.5220/0010913600003116