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AI-Powered Vision Sensing for Autonomous Driving

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 623

Special Issue Editors


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Guest Editor
California Partners for Advanced Transportation Technology (PATH), University of California, Berkeley, CA 94720, USA
Interests: connected and automated vehicles; advanced transportation systems; transit operations and optimization

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Guest Editor
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen 518055, China
Interests: unmanned aerial vehicles; intelligent transportation systems; autonomous vehicles; control systems

Special Issue Information

Dear Colleagues,

The rapid evolution of perception, decision-making, and control technologies is accelerating the deployment of autonomous driving systems across diverse real-world applications. This Special Issue seeks original research and comprehensive review articles that advance the state of the art in artificial intelligence (AI)- and vision-based sensing for autonomous vehicles. We invite contributions that push forward sensor architecting, multimodal data fusion, scene understanding, object detection and tracking, 3D reconstruction, end-to-end autonomous driving, trustworthy sensing, and reliable perception under challenging driving conditions. We particularly welcome groundbreaking work on how autonomous driving systems have been facilitated by deep learning, generative AI, and AI-agent-assisted manufacturing. Applications spanning passenger vehicles, freight transport, mobile robotics, advanced driver-assistance systems (ADASs), and infrastructure–vehicle cooperation (V2X) are within the scope of this issue. Inter-platform studies integrating vision sensing with other types of sensors, roadside perception, and cloud-based traffic information are also encouraged. This Special Issue aims to serve as an inclusive forum for researchers dedicated to the perception-and-intelligence core of autonomous driving and seeks to identify emerging trends, practical challenges, and future research directions in this dynamic domain.

Dr. Joshua H. Meng
Dr. Dachuan Li
Guest Editors

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Keywords

  • vision sensing
  • autonomous driving
  • artificial intelligence
  • data fusion
  • end-to-end system

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Published Papers (1 paper)

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Research

24 pages, 1960 KB  
Article
Discrepancy-Guided Semantic Segmentation with Boundary Detail Enhancement for Traffic Scenes
by Changshun Yu, Xiujian Yang and Shiquan Shen
Sensors 2026, 26(9), 2738; https://doi.org/10.3390/s26092738 - 28 Apr 2026
Viewed by 354
Abstract
To address the challenges of missing fine-grained objects, blurred boundaries, and the suppression of shallow details by deep semantic features during cross-scale fusion in traffic scene semantic segmentation, this paper proposes a discrepancy-guided semantic segmentation method with boundary detail enhancement. First, to improve [...] Read more.
To address the challenges of missing fine-grained objects, blurred boundaries, and the suppression of shallow details by deep semantic features during cross-scale fusion in traffic scene semantic segmentation, this paper proposes a discrepancy-guided semantic segmentation method with boundary detail enhancement. First, to improve the semantic completeness of fine-grained regions, a Gated Collaborative Context Module (GCCM) is introduced between the encoder and decoder. By leveraging gating-guided channel selection and multi-scale contextual modeling, GCCM adaptively captures semantic dependencies across different scales. Second, to alleviate boundary ambiguity and detail loss, a Frequency–Edge Guided Enhancement Module (FEGE) is designed in the decoder. This module explicitly models low-frequency structural information and high-frequency edge components via frequency decomposition, and further enhances high-frequency details using the Scharr operator and lightweight convolution, thereby improving the structural representation of object contours and boundary regions. Furthermore, to mitigate the suppression of shallow details during cross-scale feature fusion, a Discrepancy-aware Pixel-Adaptive Gating Fusion module (D-PagFM) is proposed. By jointly modeling feature similarity and local discrepancy, the module adaptively regulates pixel-wise fusion, enhancing detail integration in structurally consistent regions while suppressing misleading fusion in inconsistent regions, thereby improving the robustness of feature fusion and boundary consistency. Experimental results on the Cityscapes and CamVid datasets demonstrate that the proposed method achieves mIoU scores of 80.08% and 82.97%, respectively. Moreover, it shows more significant improvements in boundary-sensitive fine-grained categories such as road boundaries, poles, and traffic signs, indicating its effectiveness and application potential for high-precision semantic segmentation in traffic scenes. Full article
(This article belongs to the Special Issue AI-Powered Vision Sensing for Autonomous Driving)
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