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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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A data-driven subspace predictive control method for air-cooled data center thermal modelling and optimization

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Journal of the Franklin Institute 2023年第5期360卷 3657-3676页

作者： Li, Zhe Wang, Haoda Fang, Qiu Wang, Yaonan National Engineering Research Center for Robot Vision Perception and Control Technology Department of Control Science and Engineering Hunan University Hunan Changsha410082 China

This paper presents a data-driven predictive control method for optimizing the energy consumption of air-cooled data centers with unknown system model parameters. First, based on the measurable data of the studied system, the subspace predictive control (SPC) method is adopted to improve the energy use efficiency of the data center by regulating the power allocation of the server racks and the supply temperature of cold air, while ensuring the safe operating environment of the electronic equipment. Furthermore, a reasonable event-triggered law is designed to solve the problem of the low computational efficiency of the conventional SPC method. The simulation results illustrate that the designed event-triggered law can improve the computational efficiency of the algorithm while maintaining the control performance of the algorithm, which verifies its application prospect in practice. © 2023 The Franklin Institute

关键词： Computational efficiency

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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 H., Lee M., Kim J., Han J., Yoon 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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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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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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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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Frenet Frame Based Local Motion Planning in Racing Environment 23

Frenet Frame Based Local Motion Planning in Racing Environme...

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23rd International Conference on control, Automation and Systems, ICCAS 2023

作者： Kim, Min Seong Lee, Jeon Hyeok Kim, Taek Lim Park, Tae-Hyoung Chungbuk National University Department of Control and Robot Engineering Cheongju28644 Korea Republic of Chungbuk National University Department of Intelligent Systems and Robotics Cheongju28644 Korea Republic of

ISBN: (纸本)9788993215274

Local Trajectory Planning plays a pivotal role in reducing lap times during a race. It must take into account not only obstacles but also the hardware limitations of the vehicle and the performance of the controller. Moreover, in a high-speed racing competition like F1, surrounding vehicles exhibit highly dynamic movements, necessitating motion prediction for safety. However, existing studies have demonstrated good performance in environments with static objects or when avoiding racing vehicles in straight-line sections, but accidents occur in environments with large numbers of vehicles and high curvature. To address these issues, this paper proposes an algorithm that ensures stability even in complex environments by considering the uncertainty of motion prediction for multiple vehicles. By considering the uncertainty of vehicle motion, we design the cost and generate an optimized path based on sampling. The CarMaker simulator is utilized to verify the proposed algorithm. © 2023 ICROS.

关键词： Motion planning

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Camera and LiDAR Sensor Fusion Method for Object Estimation of Intelligent robots

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Journal of Institute of control, robotics and Systems 2024年第8期30卷 810-816页

作者： Lee, Sun Ho Choi, Woo Young Department of Intelligent Robot Engineering Pukyong National University Korea Republic of Department of Control and Instrumentation Engineering Pukyong National University Korea Republic of

This study proposes a low-cost camera and LiDAR sensor fusion method to improve object recognition and estimation performance in autonomous and robot systems. We first calibrate the camera and LiDAR sensor to implement the proposed method. Then, data association is performed between the bounding box data of the object identified through the YOLOv3 algorithm from the camera sensor and the LiDAR sensor data. Afterward, a Kalman filter considering sensor characteristics is applied to improve object recognition and estimation accuracy. The usefulness of the proposed method was validated through scenario-based experiments. Experimental results revealed that the proposed sensor fusion method improved object recognition and estimation accuracy, compared to methods using single sensors. © ICROS 2024.

关键词： Kalman filters

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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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Image Segmentation System of Soft Capsules Based on Deep Learning

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Journal of Institute of control, robotics and Systems 2024年第6期30卷 607-613页

作者： Jung, Young-Gil Park, Tae-Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

Pharmacists in conventional pharmacies and hospitals prepare and visually inspect medications before dispensing these medications to patients, thereby posing risks of exposure through skin contact or inhalation, as well as the potential for generating compounding errors such as incorrect medication types or quantities. To address the limitations of the manual compounding processes, automated compounding and medication-verification systems have been introduced and implemented. The automated compounding system prepares medications in pouch units based on prescriptions written by doctors, whereas the medication verification system verifies whether the types and quantities of the medication dispensed in these pouches match the prescription information. Medications are categorized broadly into solid tablets, hard capsules, and soft capsules. Color and shape information is used to classify medication types;solid tablets and hard capsules can be classified using rule-based image processing techniques such as image binarization. However, extracting shape information from soft capsules through which light passes is impossible via image binarization alone;therefore pattern matching is employed. Nevertheless, soft capsules change their forms, causing significant variations in pattern-matching similarity;therefore the detection accuracies of these capsules are low. To address this issue, this paper proposes a deep learning-based method for detecting soft capsules. © ICROS 2024.

关键词： Pattern matching

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