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Driver Lane-Changing Intention Recognition Based on Stacking Ensemble Learning in the Connected Environment: A Driving Simulator Study

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第2期25卷 1503-1518页

作者： Zhang, Hongjia Gao, Song Guo, Yingshi Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Changan Univ Sch Automobile Xian 710064 Peoples R China

The connected environment provides information on surrounding traffic and areas beyond the visual range traffic to improve driving behavior and avoid dangerous incidents. However, due to the novelty of the connected environment, there is a lack of studies on driver lane-changing intention (LCI). Therefore, this study builds a connected lane-changing scenario based on a driving simulator. The differences of the LCI feature parameters are analyzed, and a driver LCI recognition model is developed. The results show that there is a significant difference in vehicle motion parameters, driver operation parameters, and driver's eye and head movement parameters during the lane-changing left, lane-keeping, and lane-changing right stages. In addition, the length of the intention time window with connected information (6.6 s) is longer than that of the non-connected information (4.1 s). A driver LCI recognition model is developed using Stacking ensemble learning and then compared with traditional algorithms. The results show that the accuracy of the LCI recognition model using Stacking ensemble learning is higher than that of the traditional algorithms when it is recognized 0.5 s and 3 s before the lane-changing maneuver, which are 98.24% and 92.23%, respectively. The driver LCI is sent to the macro-traffic system or surrounding vehicles through connected communication, which not only helps the macro-traffic system to coordinate and control the traffic flow but also helps surrounding vehicles plan the motion trajectory in advance. In addition, the accurate recognition of driver LCI is beneficial to the transfer and distribution of the human-machine co-driving system control rights.

关键词： Connected environment lane-changing intention ensemble learning traditional algorithm co-driving

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A survey of super-resolution image quality assessment

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NEUROCOMPUTING 2025年 621卷

作者： Shu, Lei Zhu, Qinru He, Yujie Chen, Wei Yan, Jiebin Jiangxi Univ Finance & Econ Sch Virtual Real VR Modern Ind Nanchang 330013 Peoples R China Jiangxi Univ Finance & Econ Sch Comp & Artificial Intelligence Nanchang 330032 Peoples R China

Image super-resolution (SR) technology plays a crucial role in enhancing image resolution and has found extensive applications in areas such as medical imaging, surveillance, and security. The growing need for effective assessment methodologies in SR image quality (SR-IQA) has spurred significant scholarly research. This paper provides a comprehensive review of the recent advancements in SR-IQA, including both the evolution of algorithms and the creation of subjective evaluation databases. It critically examines and analyzes various SR-IQA techniques across different datasets, starting with an overview of the development of SR algorithms and highlighting the importance of SR-IQA research. The paper then introduces and compares mainstream SR-IQA datasets, categorizing and evaluating SR-IQA methodologies. It concludes with an assessment of key image quality assessment algorithms, supported by detailed dataset analyses. The conclusion summarizes the current state of SR-IQA, identifies ongoing challenges, and suggests future research directions to advance the field.

关键词： super-resolution image Review Image quality assessment Deep learning traditional algorithm

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IoT-Oriented Edge Computing-Driven Computer Vision Solution

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SPIN 2025年第0期

作者： Zhang, Songning Univ Sci & Technol China Sch Cyber Sci & Technol Hefei 230088 Anhui Peoples R China

The rise of IoT devices has led to a surge in data generation, necessitating efficient processing solutions. traditional cloud-centric approaches face challenges like latency, bandwidth and privacy issues. Edge computing, a promising paradigm, enables data processing closer to the source, enhancing IoT-driven computer vision applications. This shift integrates edge computing frameworks with a study that proposed a novel drosophila food search-tuned convolutional neural network (DFS-CNN) and computer vision algorithms for real-time tasks like object detection and anomaly detection. We collected acquisition data from labeled Faces in the Wild (LFW) video and image dataset, the acquisition data were preprocessed using a bilateral filter for minimizing noise while maintaining sharp edges, and then filtered data were extracted using a histogram of oriented gradients (HOG) - the DFS-CNN model using the HOG feature set to detection the computer vision solution. The optimal DFS-CNN model was deployed on edge computing and is used in an IoT-based architecture to compute and transport data for real-time performance simulation using Tensor Flow Lite. The proposed method is compared to other traditional algorithms. The proposed DFS-CNN model detects objects in the LWT image with 96% accuracy. The proposed DFS-CNN model was used to address students' inactivity status during the online exam, and that the outcome of the suggested method was tested with data latency, and real-time response, according to a comparative performance analysis.

关键词： Object detection algorithm LFW data model architecture processing traditional algorithm

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A Novel Method for the Driver Lane-Changing Intention Recognition

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IEEE SENSORS JOURNAL 2023年第17期23卷 20437-20451页

作者： Zhang, Hongjia Guo, Dong Guo, Yingshi Wu, Fuwei Gao, Song Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Changan Univ Key Lab Transportat Ind Automot Transportat Safet Xian 710064 Peoples R China

With the development of communication sensing technology and onboard technology, the future will enter a connected environment. Traffic information is transformed from static isolation to dynamic interconnection, and drivers' attention distribution and environmental perception abilities are changed. At present, the study on driver lane-changing intention (LCI) has received little attention. Therefore, this article presents a novel method for recognizing the driver LCI in the connected environment. First, an innovative driving simulator experiment was designed to mimic the connected environment. Second, the statistical results demonstrate that the LCI time window is 6.6 s in the connected environment, and the feature parameters of LCI are determined. These parameters include yaw rate, vehicle lateral speed, vehicle lateral acceleration, steering wheel angle, steering wheel torque, gaze direction (x, y, z), head rotation (x, y, z). Finally, a novel driver LCI recognition model is established. The phase-space reconstruction and recurrence plot techniques are used to convert the time-series feature parameters into images. The Swin transformer algorithm with state-of-the-art performance is introduced to classify the images into three categories: lane-changing left (LCL), lane keeping, and lane-changing right (LCR). This novel method overcomes the problems of vanishing gradient and low recognition accuracy caused by the long time-series input. The accuracy of the proposed LCI model is 89.90% at 3 s prior to the lane-changing maneuver, which is higher than that of the classical machine learning algorithms. This study contributes to the accurate response of lane-changing assistance systems and the smooth transfer of control rights in human-machine co-driving systems.

关键词： Connected environment lane-changing intention (LCI) Swin transformer traditional algorithm

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Syphilis in pregnancy: an ongoing public health threat

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AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY 2022年第6期227卷 822-838页

作者： Eppes, Catherine S. Stafford, Irene Rac, Martha Baylor Coll Med Dept OB Gyn Div Maternal Fetal Med Houston TX 77030 USA Univ Texas Hlth Sci Ctr Houston Dept OB Gyn Div Maternal Fetal Med Houston TX 77030 USA

Syphilis is a treponemal infection that can be acquired sexually, hematogenously, or via vertical transmission from mother to infant. Despite evidence-based curative treatment options with penicillin, it remains a public health threat with increasing prevalence over recent years. Congenital syphilis, a condition where a fetus acquires the infection during pregnancy, can lead to stillbirth, miscarriage, preterm birth, birth defects, and lifelong physical or neurologic changes. Congenital syphilis rates in the United States increased by 261% from 2013 to 2018 and continue to increase in 2021. The only recommended treatment for syphilis in pregnancy is benzathine penicillin G because evidence of decreased risk of congenital syphilis with other modalities is lacking. Testing for syphilis is complex and includes either the reverse-sequence algorithm or the traditional algorithm. Determination of the clinical stage of syphilis includes incorporation of the previous treatment sequence and physical examination. The goal of this review was to discuss the current evidence about optimal treatment and testing during pregnancy to optimize maternal health and prevent congenital syphilis.

关键词： congenital syphilis fetal syphilis reverse sequence syphilis during pregnancy syphilis testing syphilis treatment traditional algorithm treponemal testing

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Overview of pedestrian detection based on infrared image 41

Overview of pedestrian detection based on infrared image

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第41届中国控制会议

作者： Lile Huo Yong Wang Ting Liu Yunsheng Fan College of Marine Electrical Engineering Dalian Maritime University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Object detection is a research hotspot in theory and application in recent *** the development of infrared imaging technology,it can be used normally in various harsh detection environments(strong light irradiation,dark environment,smoke occlusion),which makes the pedestrian detection algorithm based on infrared image have great research and application *** to the development of infrared pedestrian detection technology,this paper will describe the traditional detection algorithm and the object detection algorithm based on deep ***,the traditional detection algorithm flow is described,and the milestone detectors are ***,the foundation laying process of two main algorithm schools of infrared pedestrian detection based on deep learning is reviewed,including two-stage algorithm represented by R-CNN series and one-stage algorithm represented by YOLO and SSD,Finally,through the existing problems,the future research of infrared pedestrian detection technology is prospected.

关键词： Object detection Infrared pedestrian detection traditional algorithm Deep learning

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Research on robot path planning methods

Research on robot path planning methods

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作者： Tianhao Chen Guanhong Jiang Department of Computer science and engineering Shanghai University Department of Science Hong Kong Baptist University

Mobile robots are used extensively across a variety of industries and *** to discover a start-to-end path without colliding has become a hot topic in recent years due to the complexity and uncertainty of the *** various environments,a path planning technique should demonstrate high efficiency and *** this can reduce the energy consumption of the robots and greatly increase their working *** paper will conclude the presently popular path planning *** on the different features of these algorithms,they are divided into three types:traditional path planning algorithm,neural-work-based algorithm,and sampling-based *** on the new papers in these years,detailed introduction of the algorithms and their variants will be *** the end of the paper,the thesis is summarized and the future research trend is prospected.

关键词： path planning traditional algorithm neural-work-based algorithm sampling-based algorithm

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Automatic pavement damage predictions using various machine learning algorithms: Evaluation and comparison

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RESULTS IN ENGINEERING 2022年 16卷

作者： Nyirandayisabye, Ritha Li, Huixia Dong, Qiming Hakuzweyezu, Theogene Nkinahamira, Francois Fujian Univ Technol Sch Civil Engn Fuzhou 350108 Fujian Peoples R China Chinese Acad Sci Inst Rock & Soil Mech Wuhan 430071 Hubei Peoples R China Harbin Inst Technol Shenzhen Sch Civil & Environm Engn State Key Lab Urban Water Resource & Environm Shenzhen Key Lab Organ Pollut Prevent & Control Shenzhen 518055 Peoples R China

In recent years, different technologies based on Machine Learning (ML) algorithms have been developed to evaluate road distress quality and help with pavement maintenance, traffic safety, and road rehabilitation. However, the literature lacks a comparative study of various ML algorithms to detect pavement damage. This study aims to fill that gap by comparing various regression algorithms including Linear Regression (LR), Support Vector Regressor (SVR), Random Forest Regressor (RFR), k-nearest neighbors, Gradient Boosting Regressor (GBR), Light Gradient Boosted Machine (LGBM), stacking regressor, and Decision Tree Regressor (DTR). To evaluate the accuracy of the models, different performance metrics, namely coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are used. Experiments are conducted to validate the models' performance using a dataset sponsored by the Michigan Department of Transportation and executed at Michigan State University for the Michigan road pavement damage. Obtained results indicate that the GBR modeling algorithm performs better with the most accurate asphalt pavement damage model of R2 = 99%, MAE = 0.02, and RMSE = 0.03. Thus, the use of GBR modeling might be an easy, robust, and efficient method for automated pavement damage prediction.

关键词： Pavement damage prediction Regression algorithm Gradient boosting regressor (GBR) traditional algorithm Assemble algorithm Pavement damage machine learning (ML)

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Syphilis: Understanding traditional and Reverse Screening algorithms

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JNP-JOURNAL FOR NURSE PRACTITIONERS 2020年第5期16卷 359-362页

作者： Thomas, Jacqueline F. Catlin, Shayria Stacey, Jonathan A. Univ West Florida Sch Nursing Pensacola FL 32514 USA Family Hlth Mobile AL USA Univ West Florida Hlth Adm Pensacola FL USA

Interpreting syphilis screening tests can be a challenge for health care providers. Clinical laboratories primarily use the reverse algorithm to screen for syphilis;however, the Centers for Disease Control and Prevention recommend the traditional algorithm. Both algorithms require the interpretation of 2 different serologic assays for screening and confirmation. Serologic testing used in both algorithms is reviewed for nurse practitioners to correctly interpret test results to establish a timely diagnosis and provide stageappropriate treatment. (C) 2020 Elsevier Inc. All rights reserved.

关键词： nontreponemal reverse algorithm syphilis traditional algorithm treponemal

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Finger Vein Recognition Based on Deep Learning 12

Finger Vein Recognition Based on Deep Learning

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12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liu, Wenjie Li, Weijun Sun, Linjun Zhang, Liping Chen, Peng Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Cognit Comp Technol Wei Fu Joint Lab Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Microelect Beijing 100029 Peoples R China

ISBN: (纸本)9781509061617

As a new biometric technology, finger vein recognition has received much attention in recent years. Since deep learning is an end-to-end system, and has achieved very good results in such fields as face recognition and target detection, we try to apply it to finger vein recognition. In this paper, we adopt seven layers of CNN which include 5 convolution layers and 2 fully-connected layers. This network obtains a recognition rate of 99.53%, which proves to be better performing than traditional algorithm.

关键词： biometrics finger vein recognition deep learning convolutional neural network traditional algorithm

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