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337 results on '"Yu, Guizhen"'

1. Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network

Author

Li, Lei, Chen, Zhifa, Wang, Jian, Zhou, Bin, Yu, Guizhen, and Chen, Xiaoxuan

Subjects
Computer Science - Artificial Intelligence
Abstract

Recently, the application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient mineral transportation. Compared to urban structured roads, unstructured roads in mining sites have uneven boundaries and lack clearly defined lane markings. This leads to a lack of sufficient constraint information for predicting the trajectories of other human-driven vehicles, resulting in higher uncertainty in trajectory prediction problems. A method is proposed to predict multiple possible trajectories and their probabilities of the target vehicle. The surrounding environment and historical trajectories of the target vehicle are encoded as a rasterized image, which is used as input to our deep convolutional network to predict the target vehicle's multiple possible trajectories. The method underwent offline testing on a dataset specifically designed for autonomous driving scenarios in open-pit mining and was compared and evaluated against physics-based method. The open-source code and data are available at https://github.com/LLsxyc/mine_motion_prediction.git, Comment: 11 pages,6 figures

Published
2024

6. Urinary C4d and progression of kidney disease in IgA vasculitis.

Author

Yu, Guizhen, Zhao, Jie, Wang, Meifang, Chen, Yang, Feng, Shi, Li, Bingjue, Wang, Cuili, Wang, Yucheng, Jiang, Hong, and Chen, Jianghua

Subjects
*DISEASE risk factors, *PROPORTIONAL hazards models, *CHRONIC kidney failure, *ENZYME-linked immunosorbent assay, *KIDNEY diseases, *FOCAL segmental glomerulosclerosis
Abstract

Background Immunoglobulin A (IgA) vasculitis nephritis (IgAVN) is the most common secondary IgA nephropathy (IgAN). Urinary C4d have been identified associated with the development and progression in primary IgAN; however, its role in kidney disease progression of IgAVN is still unclear. Methods This study enrolled 139 patients with IgAVN, 18 healthy subjects, 23 focal segmental glomerulosclerosis patients and 38 IgAN patients. Urinary C4d levels at kidney biopsy were measured using enzyme-linked immunosorbent assay. The association between urinary C4d/creatinine and kidney disease progression event, defined as 40% estimated glomerular filtration rate decline or end-stage kidney disease, was assessed using Cox proportional hazards models and restricted cubic splines. Results The levels of urinary C4d/creatinine (Cr) in IgAVN and IgAN patients were higher than in healthy controls. Higher levels of urinary C4d/Cr were associated with higher proteinuria and severe Oxford C lesions, and glomerular C4d deposition. After a median follow-up of 52.79 months, 18 (12.95%) participants reached composite kidney disease progression event. The risk of kidney disease progression event was higher with higher levels of Ln(urinary C4d/Cr). After adjustment for clinical data, higher levels of urinary C4d/Cr were associated with kidney disease progression in IgAVN [per Ln-transformed urinary C4d/Cr, hazard ratio 1.573, 95% confidence interval (CI) 1.101–2.245; P  = .013]. Compared with the lower C4d/Cr group, the hazard ratio was 5.539 (95% CI 1.135–27.035; P  = .034) for the higher levels group. Conclusions Higher levels of urinary C4d/Cr were associated with kidney disease progression event in patients with IgAVN. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Research on Hardware-in-the-Loop Test System for the Main Control Unit of Unmanned Vehicles

Author

Ni, Haoyuan, Yu, Guizhen, Zhou, Bin, Liu, Guoqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, and Fu, Yongling, editor

Published
2021
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8. Research on Open-pit Mine Virtual Environment Construction and Working Vehicle Modeling Simulation Based on PreScan

Author

Guo, Yaxin, Yu, Guizhen, Zhou, Bin, Wang, Yucan, Liu, Guoqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, and Fu, Yongling, editor

Published
2021
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10. Research on Vehicle Forward Pedestrian Recognition Based on Multi-line LIDAR

Author

Guo, Chenyang, Yu, Guizhen, Zhang, Li, Niu, Huan, Zhou, Bin, Wang, Zhangyu, Li, Da, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2020
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11. Research on Vehicle Forward Target Recognition Algorithm Based on Vision and MMW Radar Fusion

Author

Yu, Guizhen, Zhang, Sijia, Niu, Huan, Zhou, Bin, Liu, Guoqiang, Li, Da, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2020
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12. A Vision-Based Method for Vehicle Forward Collision Warning

Author

Zhang, Yanfei, Wang, Zhangyu, Zhou, Bin, Yu, Guizhen, Hu, Chaowei, Zhang, Li, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2020
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13. Vision-Based Vehicle Detection in Foggy Days by Convolutional Neural Network

Author

Yu, Guizhen, Wang, Sifen, Li, Mingxing, Guo, Yaxin, Wang, Zhangyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2020
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34. Integrated Ramp Metering Design and Evaluation Platform with Paramics

Author

Chu, Lianyu, Recker, Will, and Yu, Guizhen

Subjects
Ramp Metering, Paramics
Abstract

Ramp metering has been recognized as an effective freeway management strategy to either avoid or ameliorate freeway traffic congestion by limiting access to the freeway. California has applied ramp metering widely in major metropolitan areas. Currently, California has three major ramp metering systems: San Diego Ramp Metering System (SDRMS), Semi-Actuated Traffic Management System (SATMS), and Traffic Operations System (TOS). Although the ramp metering algorithms that underlay these systems are based on relatively simple theoretical concepts, these real-world ramp metering systems are significantly complicated by the need to tailor their deployment to handle a variety of conditions.

Published
2009

49. Modelling personalised car-following behaviour: a memory-based deep reinforcement learning approach

Author

Liao, Yaping, Yu, Guizhen, Chen, Peng, Zhou, Bin, and Li, Han

Abstract

To adapt to human-driving habits, this study develops a personalised car-following model via a memory-based deep reinforcement learning approach. Specifically, Twin Delayed Deep Deterministic Policy Gradients (TD3) is integrated with a long short-term memory (LSTM) (abbreviated as LSTM-TD3). Using the NGSIM dataset, unsupervised learning-based clustering and data feature analyses are performed. The driving characteristics related to safety, efficiency and comfort are extracted for different driving styles, i.e. aggressive, common and conservative. Then, reward functions are constructed for different driving styles by incorporating their driving characteristics. By resorting to the TD3 policy within a recurrent actor–critic framework, LSTM-TD3 optimises the car-following behaviour via trial-and-error interactions according to the reward functions. Results show that compared with LSTM-DDPG and DDPG, LSTM-TD3 reproduces personalised car-following behaviour with desirable convergence speed and reward. It reveals that LSTM-TD3 can reflect the essential difference in safety, efficiency and comfort requirements among different driving styles.

Published
2024
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50. SwinURNet: Hybrid Transformer-CNN Architecture for Real-Time Unstructured Road Segmentation

Author

Wang, Zhangyu, Liao, Zhihao, Zhou, Bin, Yu, Guizhen, and Luo, Wenwen

Abstract

Semantic segmentation is a crucial component of autonomous driving. However, the segmentation performance in unstructured roads is challenging owing to the following reasons: 1) irregular shapes and varying sizes of road boundaries; 2) low-contrast or blurred boundaries between the road and background; and 3) environmental factors such as changing light intensities and dust particles. To overcome these challenges, this study proposes SwinURNet, a transformer-convolutional neural network (CNN) architecture for real-time point cloud segmentation in unstructured scenarios. First, the point cloud is projected onto a range image via spherical projection. Then, a lightweight ResNet34-based network is designed for encoding abstract features. A nonsquare Swin transformer (NSST) is designed for decoding information and capturing high-resolution transverse features. A multidimensional information fusion module (MDIFM) is introduced to balance the semantic differences between feature maps in CNNs and attention maps in transformers. A multitask loss function (MTLF) comprising boundary, weighted cross-entropy, and Lovász–Softmax losses is introduced at the network end to guide network training. Experimental data from autonomous mining trucks in the Baiyuneboite mining area is used to evaluate the performance on unstructured roads. The proposed architecture is also applied to the public unstructured dataset RELLIS-3D and the large structured dataset SemanticKITTI. The experimental results show performance gains of 74.2%, 42.6%, and 61.6% in accuracy and 8–19 FPS inference speed with the proposed architecture, surpassing those of the compared methods.

Published
2024
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