A Safe Decision Making Framework for Automated Vehicle Navigation among Human Drivers

Vijayakumar, H., Zhao, D. , Lan, J. , Zhao, W., Tian, D. and Zhang, Y. (2023) A Safe Decision Making Framework for Automated Vehicle Navigation among Human Drivers. In: 22nd IFAC World Congress (IFAC 2023), Yokohama, Japan, 09-14 Jul 2023, pp. 4910-4915. (doi: 10.1016/j.ifacol.2023.10.1263)

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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.

Item Type:Conference Proceedings
Additional Information:This work was sponsored in part by the Engineering and Physical Sciences Research Council of the UK under the EPSRC Innovation Fellowship (EP/S001956/2) and in part by the Royal Society- New- ton Advanced Fellowship under Grant NAF/R1/201213. Jianglin Lan was supported by a Leverhulme Trust Early Career Fellowship under Award ECF-2021-517.
Keywords:Automated vehicles, decision making, motion prediction, uncertainties, planning.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong and Vijayakumar, Hari and Lan, Dr Jianglin
Authors: Vijayakumar, H., Zhao, D., Lan, J., Zhao, W., Tian, D., and Zhang, Y.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
ISSN:2405-8963
Published Online:22 November 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in IFAC-PapersOnLine 56(2):4910-4915
Publisher Policy:Reproduced under a Creative Commons license
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
314774Toward Energy Efficient Autonomous Vehiciles via Cloud-Aided learningDezong ZhaoEngineering and Physical Sciences Research Council (EPSRC)EP/S001956/2ENG - Autonomous Systems & Connectivity
314249Decarbonising Machine Learning for Safe and Robust Autonomous SystemsJianglin LanLeverhulme Trust (LEVERHUL)ECF-2021-517ENG - Autonomous Systems & Connectivity