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AI-Powered Vision Sensing for Autonomous Driving
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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 May 2027 | Viewed by 3895

Editors

Dr. Joshua H. Meng

E-Mail Website
Guest Editor
California Partners for Advanced Transportation Technology (PATH), University of California, Berkeley, CA 94720, USA
Interests: connected and autonomous vehicles; smart road sensors; cyber-physical transportation systems
Dr. Dachuan Li

E-Mail Website
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

Manuscript Submission Information

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Keywords

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

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Published Papers (5 papers)

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Research

23 pages, 4175 KB  
Article
BEV-Nexus: BEV Perception Algorithm Based on Depth Perception Enhancement and Dynamic Adaptive Fusion
by Xiaona Song, Haozhe Zhang, Zhengyi Huang, Jianlin Zhao and Lijun Wang
Sensors 2026, 26(15), 4720; https://doi.org/10.3390/s26154720 - 25 Jul 2026
Viewed by 353
Abstract
This paper proposes an improved multimodal fusion framework for 3D object detection, termed BEV-Nexus, which aims to address the issues of inaccurate depth estimation and inefficient fusion paradigms in existing image-point cloud fusion methods. We introduce a Point-Cloud-Guided Depth Prediction Network (PCGD-Net), which [...] Read more.
This paper proposes an improved multimodal fusion framework for 3D object detection, termed BEV-Nexus, which aims to address the issues of inaccurate depth estimation and inefficient fusion paradigms in existing image-point cloud fusion methods. We introduce a Point-Cloud-Guided Depth Prediction Network (PCGD-Net), which enhances the image branch’s depth prediction capability by embedding point cloud spatial prior, ground-truth loss constraint, and projected point cloud depth filling. Additionally, we design a Dynamic Self-adaptive Feature Fusion Module (DSF-Module), which computes multimodal feature similarity using window attention and performs weighted fusion based on self-adaptive weights, resolving alignment deviations in BEV features. Finally, we propose a Dilated Attention Enhancement Block (DAEB), which expands the receptive field through dilated convolution and integrates parameter-free attention mechanism (SimAM) for feature enhancement, ensuring efficiency while improving overall feature representation. Experimental results on nuScenes validation set show that BEV-Nexus outperforms it baseline (BEVFusion) by 1.8% mAP and 1.5% NDS. On the test set, BEV-Nexus improves mAP and NDS by 1.6% and 1.4%, respectively. Furthermore, the detection FPS remains nearly unchanged, demonstrating significant lightweight advantages. Full article
(This article belongs to the Special Issue AI-Powered Vision Sensing for Autonomous Driving)
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37 pages, 12135 KB  
Article
A Hierarchical VLM-to-TD3 Framework with Novel Object Coordinate Estimation and Persistent Spatial Memory for Semantically Guided Indoor Navigation
by Yernar Akhmetbek, Ayaulym Parmash, Temirlan Meiramkhanov, Azamat Yesmukhametov, Aigul Meirmanova and Darkhan Zholtayev
Sensors 2026, 26(15), 4661; https://doi.org/10.3390/s26154661 - 23 Jul 2026
Viewed by 1063
Abstract
Autonomous semantic indoor navigation requires robust low-level control and high-level understanding of objects and spatial context in cluttered and partially occluded environments. While deep reinforcement learning (DRL) methods such as twin delayed deep deterministic policy gradient (TD3) enable reactive obstacle avoidance, they typically [...] Read more.
Autonomous semantic indoor navigation requires robust low-level control and high-level understanding of objects and spatial context in cluttered and partially occluded environments. While deep reinforcement learning (DRL) methods such as twin delayed deep deterministic policy gradient (TD3) enable reactive obstacle avoidance, they typically struggle with long-horizon semantic navigation, where object-location memory and language-level reasoning are required. We present a lightweight hierarchical two-stage framework that, to the best of our knowledge, is introduced for the first time to integrate a locally deployed vision–language model (VLM), semantic object coordinate memory, and a TD3-based DRL controller for language-conditioned indoor navigation. In Stage 1, the robot performs semantic exploration using odometry, 2D LiDAR, and VLM-based object recognition to build a geometric map and store detected object categories with their estimated world coordinates in a structured javaScript object notation (JSON) semantic memory. In Stage 2, a natural language query is used to retrieve the target object coordinates from memory and pass them to a TD3 target point navigation policy, which performs mapless navigation using odometry and RealSense RGB-D perception. The proposed framework combines open-vocabulary VLM-based object coordinate estimation, LiDAR mapping, RGB-D perception, and language grounding within a unified semantic memory representation. Experiments in a ROS-integrated realistic simulation demonstrate consistent goal-reaching performance and improved navigation efficiency compared with an Artificial Potential Field baseline using the same VLM and a DRL + GPT-4o mini configuration. We also compare the proposed VLM-based recognition module with YOLO-World v2.6 and Grounding DINO, showing that the VLM-based approach provides more reliable semantic grounding and target-coordinate estimation in the tested indoor navigation scenarios, particularly for flexible natural language object queries. Full article
(This article belongs to the Special Issue AI-Powered Vision Sensing for Autonomous Driving)
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28 pages, 33384 KB  
Article
DER-YOLO: A Lightweight Stage-Wise Feature Calibration Network for Onboard Real-Time Small-Object Detection
by Jiapei Wei, Azizan As’arry, Khairil Anas Md Rezali, Mohd Zuhri Mohamed Yusoff, Masnida Hussin and Tong Mu
Sensors 2026, 26(14), 4462; https://doi.org/10.3390/s26144462 - 14 Jul 2026
Viewed by 418
Abstract
Accurate real-time detection of small traffic objects remains a critical challenge for onboard vision-based traffic perception, particularly under conditions of weak texture, scale variation, occlusion, and limited computational resources. To address these challenges, this paper proposes DER-YOLO, a lightweight small-object-oriented detector built upon [...] Read more.
Accurate real-time detection of small traffic objects remains a critical challenge for onboard vision-based traffic perception, particularly under conditions of weak texture, scale variation, occlusion, and limited computational resources. To address these challenges, this paper proposes DER-YOLO, a lightweight small-object-oriented detector built upon YOLO11n, specifically designed for complex traffic scenes. DER-YOLO introduces stage-wise feature calibration across the backbone, neck, and pre-head stages to enhance small-object representation. First, a Decoupled Global Context C3k2 (DGC-C3k2) module strengthens contextual representation for weak and low-saliency traffic objects after local feature extraction. Second, an ECA-guided Cross-scale Adaptive Fusion (ECAF) module adaptively balances high-level semantic cues and shallow high-resolution details to improve multi-scale feature interaction. Third, a Refined Large Selective Kernel (RLSK) module refines high-resolution spatial responses before the P3 detection head, enhancing small-object localization. Extensive experiments on KITTI and BDD100K demonstrate that DER-YOLO improves detection accuracy while maintaining real-time inference. On KITTI, it achieves 86.95% mAP@0.5 and 60.89% mAP@0.5:0.95, with small-object AP@0.5:0.95 increasing from 27.0% to 29.3%. On BDD100K, it achieves 55.05% mAP@0.5 and 29.21% mAP@0.5:0.95, with small-object AP@0.5:0.95 increasing from 12.6% to 15.2%. With 2.744 M parameters, 7.401 GFLOPs, and over 100 FPS, DER-YOLO provides an effective and lightweight solution for real-time small-object detection in onboard traffic perception scenarios. Full article
(This article belongs to the Special Issue AI-Powered Vision Sensing for Autonomous Driving)
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26 pages, 3824 KB  
Article
Lightweight Monocular Distance Estimation via Anisotropic Geometry Loss for Low-Light Driving Environments
by Ricky Christanto and Shaou-Gang Miaou
Sensors 2026, 26(14), 4440; https://doi.org/10.3390/s26144440 - 13 Jul 2026
Viewed by 483
Abstract
Robust monocular distance estimation under varying illumination conditions is critical for autonomous driving safety. While state-of-the-art monocular 3D detection models achieve high accuracy in daylight conditions, they rely on computationally heavy architectures and degrade significantly in low-light environments. Lightweight 2D detectors (e.g., YOLO [...] Read more.
Robust monocular distance estimation under varying illumination conditions is critical for autonomous driving safety. While state-of-the-art monocular 3D detection models achieve high accuracy in daylight conditions, they rely on computationally heavy architectures and degrade significantly in low-light environments. Lightweight 2D detectors (e.g., YOLO variants) offer real-time performance but lack the geometric constraints required for accurate depth estimation. To address this limitation, we propose the Anisotropic Geometry Loss (AGL) framework. This lightweight framework enforces ground-plane consistency through an anisotropic bottom-edge constraint derived from the pinhole camera model. In addition, a luminance-channel contrast enhancement module (CLAHE) is applied at inference to improve low-light visibility. Experimental results on the Dark-KITTI dataset show that the proposed method achieves an RMSE of 10.91 ± 0.68 m, improving over YOLOv10n (11.53 ± 0.56 m) and YOLOv26n (11.99 ± 0.58 m), while maintaining a 2.71 M-parameter footprint and real-time inference (>160 FPS). With CLAHE, RMSE is further reduced to 10.55 ± 0.72 m. Stratified by kinematic safety zone, the proposed method achieves 2.42 ± 0.03 m in the Near range (0–15 m), 5.94 ± 0.19 m in the Medium range (15–30 m), and 17.41 ± 1.25 m in the Far range (>30 m), corresponding to Euro NCAP AEB (Autonomous Emergency Braking) stopping distances. AGL provides its largest measurable accuracy improvement in the medium-distance range while maintaining comparable performance in the far-distance range. A complementary luminance-channel CLAHE preprocessor recovers bottom-edge gradients in synthetic and real low-light frames; zero-shot generalization is qualitatively corroborated on the ExDark dataset. These results demonstrate that explicit geometric constraints provide an effective and efficient solution for robust cross-illumination resistance in monocular distance estimation. The framework also shows practical potential for camera-only AEB systems deployed on edge-computing platforms aligned with Euro NCAP safety protocols. Full article
(This article belongs to the Special Issue AI-Powered Vision Sensing for Autonomous Driving)
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23 pages, 8853 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 710
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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