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Lookup NU author(s): Dr Husnain SheraziORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 by the authors. Autonomous vehicles (AVs) play a crucial role in enhancing urban mobility within the context of a smarter and more connected urban environment. Three-dimensional object detection in AVs is an essential task for comprehending the driving environment to contribute to their safe use in urban environments. Existing 3D LiDAR object detection systems lose many critical point features during the down-sampling process and neglect the crucial interactions between local features, providing insufficient semantic information and leading to subpar detection performance. We propose a dynamic feature abstraction with self-attention (DFA-SAT), which utilizes self-attention to learn semantic features with contextual information by incorporating neighboring data and focusing on vital geometric details. DFA-SAT comprises four modules: object-based down-sampling (OBDS), semantic and contextual feature extraction (SCFE), multi-level feature re-weighting (MLFR), and local and global features aggregation (LGFA). The OBDS module preserves the maximum number of semantic foreground points along with their spatial information. SCFE learns rich semantic and contextual information with respect to spatial dependencies, refining the point features. MLFR decodes all the point features using a channel-wise multi-layered transformer approach. LGFA combines local features with decoding weights for global features using matrix product keys and query embeddings to learn spatial information across each channel. Extensive experiments using the KITTI dataset demonstrate significant improvements over the mainstream methods SECOND and PointPillars, improving the mean average precision (AP) by 6.86% and 6.43%, respectively, on the KITTI test dataset. DFA-SAT yields better and more stable performance for medium and long distances with a limited impact on real-time performance and model parameters, ensuring a transformative shift akin to when automobiles replaced conventional transportation in cities.
Author(s): Mushtaq H, Deng X, Ali M, Hayat B, Sherazi HHR
Publication type: Article
Publication status: Published
Journal: Sustainability
Year: 2023
Volume: 15
Online publication date: 13/09/2023
Acceptance date: 07/09/2023
Date deposited: 23/05/2024
ISSN (electronic): 2071-1050
Publisher: MDPI
URL: https://doi.org/10.3390/su151813667
DOI: 10.3390/su151813667
Data Access Statement: The dataset created and examined in the present study can be accessed from the KITTI 3D object detection repository (https://www.cvlibs.net/datasets/kitti/eval_object. php?obj_benchmark=3d, accessed on 14 March 2023).
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