Singh, R., Mozaffari, S., Rezaei, M. orcid.org/0000-0003-3892-421X et al. (1 more author) (2023) LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application. In: 2023 International Symposium on Signals, Circuits and Systems (ISSCS). 2023 International Symposium on Signals, Circuits and Systems (ISSCS), 13-14 Jul 2023, Iasi, Romania. IEEE ISBN 9798350342031
Abstract
The development of Adaptive Cruise Control (ACC) systems aims to enhance the safety and comfort of vehicles by automatically regulating the speed of the vehicle to ensure a safe gap from the preceding vehicle. However, conventional ACC systems are unable to adapt themselves to changing driving conditions and drivers' behavior. To address this limitation, we propose a Long Short-Term Memory (LSTM)based ACC system that can learn from past driving experiences and adapt and predict new situations in realtime. The model is constructed based on the real-world highD dataset, acquired from German highways with the assistance of camera-equipped drones. We evaluated the ACC system under aggressive lane changes when the side lane preceding vehicle cut off, forcing the targeted driver to reduce speed. To this end, the proposed system was assessed on a simulated driving environment and compared with a feedforward Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model. The results show that the LSTM-based system is 19.25 % more accurate than the ANN model and 5.9 % more accurate than the MPC model in terms of predicting future values of subject vehicle acceleration. The simulation is done in Matlab/Simulink environment.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Nov 2023 11:58 |
Last Modified: | 10 Nov 2023 12:10 |
Published Version: | https://ieeexplore.ieee.org/document/10190899 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/isscs58449.2023.10190899 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205145 |