Communication delay-aware cooperative adaptive cruise control with dynamic network topologies-a convergence of communication and control

Liu, Jihong; Zhou, Yiqing; Liu, Ling

doi:10.1016/j.dcan.2023.07.004

Digital Communications and Networks

2023

DOI: 10.1016/j.dcan.2023.07.004

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Communication delay-aware cooperative adaptive cruise control with dynamic network topologies-a convergence of communication and control

Jihong Liu

Yiqing Zhou

Ling Liu

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2024

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Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors

Tang,

Zhou,

et al. 2024

Sensors

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Occlusion presents a major obstacle in the development of pedestrian detection technologies utilizing computer vision. This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. This technique enhances both channel-level and spatial-level features concurrently without incurring additional annotation costs. Furthermore, we transition from a traditional one-to-one correspondence between proposals and predictions to a one-to-multiple paradigm, facilitating non-maximum suppression using the prediction set as the fundamental unit. Additionally, we integrate these methodologies by aggregating local features between regions of interest (RoI) through the reuse of classification weights, effectively mitigating false positives. Our experimental evaluations on three widely used datasets demonstrate that AGFEN achieves a 2.38% improvement over the baseline detector on the CrowdHuman dataset, underscoring its effectiveness and potential for advancing pedestrian detection technologies.

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Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors

Tang,

Zhou,

et al. 2024

Sensors

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show abstract

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Communication delay-aware cooperative adaptive cruise control with dynamic network topologies-a convergence of communication and control

Cited by 1 publication

References 27 publications

Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors

Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors

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