Bonyani, M, Rahmanian, M, Jahangard, S et al. (1 more author) (Cover date: September 2023) DIPNet: Driver Intention Prediction for a Safe Takeover Transition in Autonomous Vehicles. IET Intelligent Transport Systems, 17 (9). pp. 1769-1783. ISSN 1751-956X
Abstract
Following the successful development of advanced driver assistance systems (ADAS), the current research directions focus on highely automated vehicles aiming at reducing human driving tasks, and extending the operational design domain, while maintaining a higher level of safety. Currently, there are high research demands in academia and industry to predict driver intention and understating driver readiness, e.g. in response to a “take-over request” when a transition from automated driving mode to human mode is needed. A driver intention prediction system can assess the driver's readiness for a safe takeover transition. In this study, a novel deep neural network framework is developed by adopting and adapting the DenseNet, long short-term memory, attention, FlowNet2, and RAFT models to anticipate the diver maneuver intention. Using the public “Brain4Cars” dataset, the driver maneuver intention will be predicted up to 4 s in advance, before the commencement of the driver's action. The driver intention prediction is assessed based on 1) in-cabin 2) out-cabin (road) and 3) both in-out cabin video data. Utilizing K-fold cross-validation, the performance of the model is evaluated using accuracy, precision, recall, and F1-score metrics. The experiments show the proposed DIPNet model outperforms the state-of-the-art in the majority of the driving scenarios.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes (https://creativecommons.org/licenses/by-nc/4.0/). |
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) |
Funding Information: | Funder Grant number EU - European Union 101006664 |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Apr 2023 14:44 |
Last Modified: | 08 Nov 2023 13:59 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1049/itr2.12370 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198233 |