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Camera-LiDAR Fusion for Detecting Traffic Law Violations in Surveillance Systems

Journal of Institute of control, robotics and Systems 2025年第3期31卷 187-195页

作者： Kim, Taek-Lim Jang, Byoung-Jin Yeon, Joo-Yeon Park, Tae Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Korea Republic of

Traffic surveillance systems are essential for ensuring public safety and optimizing urban traffic flow by accurately detecting, classifying, and monitoring traffic law violations such as illegal parking and jaywalking. However, detecting such violations in dynamic urban environments poses significant challenges due to unpredictable conditions. Traditional camera-based systems excel in capturing visual details but suffer performance degradation under low light, occlusion, or complex intersection scenarios, leading to false positives and missed detections. Therefore, we propose a camera-LiDAR fusion-based traffic violation detection system. LiDAR's independence from lighting conditions complements camera capabilities, especially in night scenarios or occluded environments. Experimental results in a real-world urban environment demonstrate the system’s effectiveness in reducing false positives and missed detections compared to traditional camera-only approaches. This study underscores the potential of sensor fusion to improve traffic management systems and points out future research opportunities in challenging conditions. © ICROS 2025.

关键词： Urban transportation

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Reinforcement Learning for Highway Driving Decision-making Using CDR (Curriculum based Domain Randomization)

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Journal of Institute of control, robotics and Systems 2025年第2期31卷 114-123页

作者： Yong, Jae-Hyoung Kim, Min-Sung Park, Tae-Hyoung Department of Intelligent System and Robotics Chungbuk National University Korea Republic of Department of Control Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Chungbuk National University Hospital Korea Republic of

Incorrect autonomous driving decisions on highways can lead to traffic congestion and accidents. Therefore, accurate decision-making in highways is essential. However, decision-making in highways is a challenging task due to the complexity of traffic situations. Thus, reinforcement learning is an appropriate approach for decision-making in highways. However, a domain gap often arises between the training and testing environments due to differences in them. Therefore, a curriculum-based domain randomization approach is applied, which progressively adjusts the level of random parameters. The RL model is trained using the SUMO simulator and tested in the MORAI simulator, where white noise is added to simulate real-world conditions. Consequently, applying curriculum domain randomization has the lowest collision rate. © ICROS 2025.

关键词： Traffic congestion

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Long-Range Vehicle Detection of LiDAR using Adaboost Classification

Long-Range Vehicle Detection of LiDAR using Adaboost Classif...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Lim, Na-Young Lee, Jeon-Hyeok Park, Tae-Hyoung Chungbuk National University Department of Intelligent Systems & Robotics Cheongju Korea Republic of Chungbuk National University Department of Control & Robot Engineering Cheongju Korea Republic of

ISBN: (纸本)9798331531614

In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range perception. However, current 3D LiDAR object detection methods face challenges with high computational costs and limited detection ranges. These issues make them unsuitable for racing scenarios. To address these challenges, this paper proposes a clustering-based long-range vehicle detection method that relies solely on LiDAR. First, the method removes ground points and foreground points and clusters the remaining points. Subsequently, these clusters are classified as vehicles using AdaBoost, generating 2D bounding boxes in the range view. Experimental results demonstrate superior performance, achieving a computational efficiency of 53 Hz and a long-range detection accuracy of over 80%, compared to voxel-based and range-based methods. This approach offers a viable solution for autonomous racing environments. © 2025 IEEE.

关键词： Autonomous vehicles

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Hyperbolic cosine transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory， Tianjin University of Technology Department of Electrical Engineering， Tshwane University of Technology

Recently， Transformer has achieved great success in computer vision. However， it is constrained because the spatial and temporal complexity grows quadratically with the number of large points in 3D object detection applications from point clouds. Previous point-wise methods are suffering from time consumption and limited receptive fields to capture information among points. To address these limitations， we propose the cosh-attention， which reduces the computation complexity of space and time from the quadratic order to linear order with respect to the number of points. In the cosh-attention， the traditional softmax operator is replaced by non-negative ReLU activation and hyperbolic-cosine-based operator with re-weighting mechanism. Then based on the key component， cosh-attention， we present a two-stage hyperbolic cosine transformer (ChTR3D) for 3D object detection from point clouds. It refines proposals by applying cosh-attention in linear computation complexity to encode rich contextual relationships among points. Extensive experiments on the widely used KITTI dataset and Waymo Open Dataset demonstrate that， compared with vanilla attention， the cosh-attention significantly improves the inference speed with competitive performance. Experiment results show that， among two-stage state-of-the-art methods using point-level features to refine proposals， the proposed ChTR3D is the fastest one.

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Swarming Without an Anchor (SWA): robot Swarms Adapt Better to Localization Dropouts Then a Single robot

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IEEE robotics and Automation Letters 2025年第6期10卷 6207-6214页

作者： Horyna, Jiří Jung, Roland Weiss, Stephan Ferrante, Eliseo Saska, Martin Czech Technical University in Prague Multi-Robot Systems Group Faculty of Electrical Engineering Technicka 2 Prague Czech Republic University of Klagenfurt Control of Networked Systems Institute of Smart Systems Technologies Klagenfurt Lakeside Park Austria Vrije Universiteit Amsterdam Department of Computer Science Amsterdam Netherlands New York University Abu Dhabi Department of Computer Science United Arab Emirates Dubai Future Labs Dubai United Arab Emirates

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization. © 2016 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Long-Range Vehicle Detection of LiDAR using Adaboost Classification*

Long-Range Vehicle Detection of LiDAR using Adaboost Classif...

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IEEE/SICE International Symposium on System Integration

作者： Na-Young Lim Jeon-Hyeok Lee Tae-Hyoung Park Department of Intelligent Systems & Robotics Chungbuk National University Cheongju South Korea Department of Control & Robot Engineering Chungbuk National University Cheongju South Korea

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

关键词： Laser radar Three-dimensional displays Accuracy Vehicle detection System integration Object detection Computational efficiency Faces Standards Autonomous vehicles

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Development of Mobile Manipulator System Providing Grounded Haptic Information During Gait Training for the Stroke

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Journal of Institute of control, robotics and Systems 2025年第4期31卷 265-268页

作者： Lee, Junyeong Lee, Hosu Kim, Minkyung Han, Jaeyoung Yoon, Jungwon Department of AI Convergence Gwangju Institute of Science and Technology Korea Republic of Department of Control and Robot Engineering & School of Aerospace Engineering Gyeongsang National University Korea Republic of Department of Physical Medicine and Rehabilitation Chonnam National University Hospital Korea Republic of

Effective gait training of the stroke can be performed by leveraging grounded haptic information obtain by touching an external immobile object. robotic systems with various preset curvatures allow users to undergo gait training without spatial restrictions, as well as straight-path gait training. We developed a mobile manipulator platform in which the handle is positioned along the preset gait-training path while the robot guides the user by providing grounded haptic information along the designated path as a rail. To verify the system’s functionality, experiments were conducted for various curvatures. Based on the results, the robot can guide the user along the defined track while providing grounded haptic information. Further investigation is required to verify the system’s effectiveness across a broad patient population. © ICROS 2025.

关键词： Electric grounding

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Point-voxel dual transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot La-boratory， Tianjin University of Technology， 391 Binshui West Road Department of Electrical Engineering， Tshwane University of Technology

Deep learning for point clouds faces the challenge of their inherent unordered nature， which makes traditional CNN-like methods not directly applicable. However， due to the inherent permutation invariance， transformers provide solutions to unordered points problems faced in LiDAR-based object detection. In this paper， a two-stage LiDAR 3D object detection framework is presented， namely Point-Voxel Dual Transformer (PV-DT3D)， which is a transformer-based method. In the proposed PV-DT3D， point-voxel fusion features are used for proposal refinement. Specifically， in the PV-DT3D， keypoints are sampled from entire point cloud scene and used to encode representative scene features via a proposal-aware voxel set abstraction module. Subsequently， following the generation of proposals by the region proposal networks (RPN)， the internal encoded keypoints are fed into dual transformer encoder-decoder architecture. In 3D object detection， the proposed PV-DT3D is the first to take advantage of both pointwise transformer and channel-wise architecture to capture contextual information from the perspective of spatial and channel dimensions. Experiments conducted on the highly competitive KITTI 3D car detection leaderboard show that， the PV-DT3D achieves superior detection accuracy among state-of-the-art point-voxel-based methods.

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Visualization of stress-strain distribution on fatigue crack with grain-level spatial resolution via self-magnetic flux density

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Dharmawan, I. Dewa Made Oka Lee, Jinyi Graduate School of Chosun University Department of Control Instrumentation and Robot Engineering Metropolitan City Gwangju61452 Korea Republic of Chosun University Department of Electronic Engineering Metropolitan City Gwangju61452 Korea Republic of

Fatigue crack—known for its unpredictability and rapid propagation—is influenced by various physical and chemical factors. A promising approach to understanding crack nucleation and growth involves visualizing local stresses and phase changes around defects. To achieve high-resolution mapping, ideally at the grain level, we propose a method to detect stress–strain maps through self-magnetic flux density (MFD) distribution using a linear three-axis tunnel magnetoresistive sensor that measures in all orthogonal directions. Theoretical and experimental bases for the reciprocal relationship between stress components and MFD distribution are discussed. Experiments are conducted on three specimens with artificially induced fatigue cracks generated using a compact test. The defect depths vary from 3–7 mm, at depth angles of 0°, 30°, and 60°. These experiments successfully map grain-level stress concentrations near the crack tip, identifying the stress concentration zone. This analysis should enhance our understanding of stress–strain behavior associated with fatigue cracks, enabling early detection. © 1963-2012 IEEE.

关键词： Fatigue crack

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