@inproceedings{e233c6d9c1b045b3a13ea06b5e901791,
title = "A Safe Decision Making Framework for Automated Vehicle Navigation among Human Drivers",
abstract = "In the near future, automated vehicles (AVs) will have to interact closely with Human-driven vehicles (HDVs). This work proposes an integrated decision-making framework that considers HDVs motion, a feasibility check, and planning. A learning-based encoder-decoder Long Short-Term Memory is used for HDV motion prediction. An error ellipse is used to capture the uncertainty from the learning-based model. A feasibility check is carried out to confirm the existence of a lane change trajectory from the given target vehicle's future position. The results from the feasibility check decide the action of AV. This work uses a lower-order parametric curve for path planning combined with an efficient trapezoidal acceleration-based velocity planner. Simulation results show that the proposed method guarantees a collision-free path for a lane changing scenario, given the lead vehicle position.",
keywords = "Automated vehicles, decision making, motion prediction, planning, uncertainties",
author = "Harikirshnan Vijayakumar and Dezong Zhao and Jianglin Lan and Wenjing Zhao and Daxin Tian and Yuanjian Zhang",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.1263",
language = "英语",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "4910--4915",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
address = "荷兰",
edition = "2",
}