Zhengqiong Liu

Hefei University of Technology

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Co-Authors

Momiao Zhou

Hefei University of Technology

Zhizhong Ding

Hefei University of Technology

Zhizhong Ding

Hefei University of Technology

Jian Niu

Hefei University of Technology

Conghu Liu

Shanghai Jiao Tong University

Ye Zhao

Hefei University of Technology

Mingzhou Liu

Hefei University of Technology

Pengcheng Wang

University of Houston

Fangzhe Zhu

Hefei University of Technology

Rui Sun

Hefei University of Technology

Cooperative Institutions

Hefei University of Technology

State Key Laboratory of Food Science and Technology

Nanchang University

University of Science and Technology of China

Shanghai Jiao Tong University

Fudan University

Xidian University

Chinese Academy of Sciences

Northwestern Polytechnical University

Central South University

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CNN-Based Lane-Level Positioning with Only On-Board Camera

International Journal of Sensor Networks (2024)

Zhizhong Ding Zhou Momiao Sun Yanshi Zhengqiong Liu Li Chen

10.1504/ijsnet.2024.10067172

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Label-Free Model Evaluation with Out-of-Distribution Detection

Applied Sciences (2023)

Fangzhe Zhu Ye Zhao Zhengqiong Liu Xueliang Liu

In recent years, label-free model evaluation has been developed to estimate the performance of models on unlabeled test sets. However, we find that existing methods perform poorly in environments with out-of-distribution (OOD) data. To address this issue, we propose a novel automatic model evaluation method using OOD detection to reduce the impact of OOD data on model evaluation. Specifically, we use the representation of datasets to train a neural network for accuracy prediction and employ energy-based OOD detection to exclude OOD data during testing. We conducted experiments on several benchmark datasets with varying amounts of OOD data (SVHN, ISUN, ImageNet, and LSUN) and demonstrated that our method reduces the RMSE compared to existing methods by at least 1.27%. Additionally, we tested our method on transformed datasets and datasets with a high proportion of OOD data, and the results show its robustness.

Robustness

Benchmark (surveying)

10.3390/app13085056

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Citations (1)

EDA Research of Oscillator

Modern Electronics Technique (2007)

Zhengqiong Liu

With the computer technical development,the EDA technique gets an extensive application in the electronic circuit design.Oscillator is an essential constitute part in the modern electronics system.For the overall understanding of oscillation capability,using EDA tool oscillation process,establish process of autovoltage,effct of oscillation capability with varying the value of component are simulated and analyzed,it can simplity the design process of the engineering of oscillator and provide rest on optimal design.

Oscillation (cell signaling)

Component (thermodynamics)

Electronic component

Source

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Assembly process control method for remanufactured parts with variable quality grades

The International Journal of Advanced Manufacturing Technology (2015)

Mingzhou Liu Conghu Liu Lingling Xing Zhengqiong Liu Xiaojing Li

Remanufacturing

10.1007/s00170-015-8026-x

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Citations (26)

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering (2022)

Chenyu Song Momiao Zhou Zhizhong Ding Zhengqiong Liu Han Cheng

More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

10.1177/09544070221121835

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Citations (1)

Performance Evaluation of Hybrid Distributed-Centralized TDMA in High-Density Vehicular Networks

IEEE Communications Letters (2022)

Momiao Zhou Min Wu Zhizhong Ding Zhengqiong Liu Feifei Zhao

This letter considers the hybrid of distributed and centralized time division multiple access (TDMA) mechanism in high-density vehicular networks, where vehicles within a certain road section perform distributed access based on the slot map broadcast by a centralized unit, i.e., road side unit (RSU). By both theoretical analysis and simulation, we mainly explore how the medium access control (MAC) performance is affected by the continuous vehicle entering and leaving the road section. First, we express the

$n$

-step transition probability of the number of vehicles that have successfully acquired a time slot, based on which the average success ratio of vehicles within

$N$

frames is calculated. Then we present the method to derive the success probability of a certain vehicle within

$N$

frames and its average access delay. Furtherly, simulations verify the correctness of our theoretical analysis, and it is shown that fast access can be ensured if the vehicles are unblocked on the road.

10.1109/lcomm.2022.3144793

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Citations (2)

Unsupervised video summarization via clustering validity index

Multimedia Tools and Applications (2019)

Ye Zhao Yanrong Guo Rui Sun Zhengqiong Liu Dan Guo

Robustness

Benchmark (surveying)

Affinity propagation

10.1007/s11042-019-7582-8

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Citations (11)

An effective channel allocation algorithm to maximize system utility in heterogeneous DCB WLANs

Computer Networks (2019)

Caihong Kai Yuting Liang Xinyue Hu Zhengqiong Liu Lusheng Wang

Channel allocation schemes

10.1016/j.comnet.2019.01.043

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Citations (8)

CNN-based lane-level positioning with only on-board camera

International Journal of Sensor Networks (2025)

Chen Li Zhengqiong Liu Momiao Zhou Yanshi Sun Zhizhong Ding

Multi camera

10.1504/ijsnet.2025.143903

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Lane Environment Detection with Only On-Board Camera

Chen Li Zhengqiong Liu Zhizhong Ding

Lane environment detection is a vital prerequisite for realizing autonomous driving in complex scenarios. However, existing methods face significant challenges, primarily containing the absence of lane-level positioning information, the imbalance between accuracy and processing speed, and the prohibitively high cost of equipment. To address these challenges, this paper introduces a hybrid method that combines deep learning and traditional image processing techniques. The proposed approach uses the YOLO (You Only Look Once) for environmental vehicle detection, augmented by a post-processing module that provides detailed lane-level positioning information. Notably, the post-processing stage integrates deep learning and traditional computer vision methods, ensuring a fast detection speed while maintaining a desirable detection accuracy. An additional advantage of the proposed method is its reliance solely on an on-board camera, effectively lowering the equipment cost. Furthermore, the method provides more comprehensive detection by incorporating geometric method to estimate the distance between the ego-vehicle and surrounding vehicles. The proposed approach addresses the aforementioned challenges and provides a comprehensive solution for effective and low-cost lane environment detection. To be specific, our proposed method runs at 58 fps and achieves accuracy at 95.22%.

On board

10.1109/eiecc60864.2023.10456663

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