@inproceedings{f4ef5a8488fd47d9bf4727ede491a7c7,
title = "Backing Up Control of a Self-Driving Truck-Trailer Vehicle with Deep Reinforcement Learning and Fuzzy Logic",
abstract = "This paper presents a control strategy for autonomous truck-trailer systems based on deep reinforcement learning (Deep RL). The deep deterministic policy gradient (DDPG) is used for training the controller using a reward function defined in terms of the desired final state of the system. A fuzzy-logic approach is employed to avoid the truck-trailer jackknife state. Simulation results show that the designed controller exhibits similar performance to state-of-the-art controllers such as the linear-fuzzy controller but with a much simpler design process.",
keywords = "control systems, deep learning, reinforcement learning, self-driving vehicles",
author = "Eduardo Bejar and Antonio Moran",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018 ; Conference date: 06-12-2018 Through 08-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ISSPIT.2018.8642777",
language = "English",
series = "2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "202--207",
booktitle = "2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018",
}