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检索条件"机构=Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle"

共 63 条记录，以下是1-10 订阅

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Research on 5G Communication Control Strategies for Autonomous Driving 4

Research on 5G Communication Control Strategies for Autonomo...

引用

4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Liu, Huanyi Guangxi Polytechnic of Construction School of Information Engineering Guangxi China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Vocational Normal University Nanning China

ISBN: (纸本)9798331534622

This paper expounds the construction of an autonomous driving wireless communication system, underscoring the pivotal function of 5G technology in providing high bandwidth and low-latency communication, as well as the advantages of edge computing in data processing and response time reduction. It introduces a multi-path data forwarding strategy that intelligently selects data transmission paths through URL parsing. This strategy, combined with an enhanced SF-RED algorithm and end-to-end dual-connection dual-link technology, effectively reduces packet loss. Furthermore, an optimized task allocation strategy for 5G edge access nodes is proposed, aimed at efficient task delegation and maximizing system utility through reduced execution duration at edge access nodes. Empirical results show that the proposed strategies significantly enhance communication quality, response speed, and control accuracy within autonomous driving systems, thereby offering strong support for the development of autonomous driving technology. © 2024 IEEE.

关键词： 5G mobile communication systems

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A study of unmanned deep learning target detection 4

A study of unmanned deep learning target detection

引用

4th International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Mo, Liye Leng, Sixuan Luo, Zhenhong Ou, Danfeng Lin, Xingzhi Key Laboratory of Application Technology of Intelligent Connected Vehicle GuangXi Vocational Normal University Guangxi Nanning China

ISBN: (纸本)9798400709753

Aiming at the technical problems of dynamic adjustment and path planning in the process of unmanned target detection, deep learning, target dataset, and target dataset-based detection model research were carried out from three aspects, namely, detection principle, model training, and model experimentation, and technical methods such as deep learning, neural network, and YOLOv5 were applied to achieve environment sensing and target detection. The YOLOv5 target detection model was applied to conduct unmanned deep learning target detection experiments, and the results show that the YOLOv5 target detection model based on the ArgoVerse training set is feasible as a technical solution for unmanned deep learning target detection system. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning Self-driving Target detection YOLO

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A study of Autonomous vehicle Behavior Decision System based on Fusion Model

A study of Autonomous Vehicle Behavior Decision System based...

引用

2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Zhang, Yongjie University GuangXi Vocational Normal University Key Laboratory of Intelligent Connected Vehicle Application Technology of Guangxi Nanning China

ISBN: (纸本)9781665457033

The commonly used behavior decision models of behavior decision systems are rule-based models and learning-based models, the former is highly interpretable and highly efficient, often used in industrial applications, but difficult to adapt to complex scenarios, the latter can solve some special scenarios of automatic driving, but the computational efficiency is low and the logic is uninterpretable, and it is rarely used in industrial applications. In this paper, the fusion architecture of behavior decision system model in autonomous driving is studied, the fusion scheme of rule model and learning model is designed, and the car is tested by autonomous driving simulation to test different behavior decision models in intersection scenarios and traffic vulnerable group scenarios, and the test results show that the fusion model scheme has better interpretability, accuracy and computational efficiency than a single model, which proves that the fusion model can solve the problems of incomplete coverage of a single rule model and excessive resource occupation of the learning model to a certain extent. © 2023 IEEE.

关键词： Autonomous vehicles

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Real-time Binocular Depth Estimation For Autonomous Driving Based on Convolutional Neural Network 5

Real-time Binocular Depth Estimation For Autonomous Driving ...

引用

5th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Liu, Jie Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Vocational Normal University Nanning China

ISBN: (纸本)9781839539848

The relative distance to other vehicles on the road is one of the key data points that an autonomous driving camera system needs to acquire. Most of the related research has primarily focused on estimating depth distances using image sequences from stereo cameras to generate depth maps for entire images. To reduce the computational complexity of depth estimation and improve real-time capabilities, this study proposes a strategy that utilizes convolutional neural network (CNN) technology in conjunction with triangulation methods for estimating the depth distance of the measured object. Initially, the CNN network structure is tailored through cropping and trained to serve as a vehicle detection model. Subsequently, this model is applied to multiple frames from the stereo camera. Following this, relative distance between the object of interest and the host vehicle is estimated using histogram similarity estimation and triangulation calculations. To validate the effectiveness of this approach, extensive simulation test was conducted on an autonomous driving platform. Experimental results demonstrate that this method exhibits a certain degree of effectiveness, with depth estimation errors being kept within an acceptable range. © The Institution of Engineering & technology 2023.

关键词： Binocular vision

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In-depth analysis of road detection for autonomous driving based on machine vision

In-depth analysis of road detection for autonomous driving b...

引用

2024 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhou, Chunjiang Wei, Lili Lin, Xingzhi GuangXi Vocational Normal University Xixiangtang District University East Road 105 GuangXi Nanning China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Xixiangtang District University East Road 105 GuangXi Nanning China

ISBN: (纸本)9781510689275

In response to the issues of target detection technology in complex environmental conditions within machine vision, research was conducted on solutions from the aspects of image preprocessing, feature extraction, and deep learning models. To address this problem, multi-scale feature fusion and attention mechanisms were employed to effectively analyze the process of road detection and obstacle recognition. The architecture module of this study achieved illumination robust target detection, precise target localization, and target classification functions. Simulation experiment results showed that the target detection accuracy reached over 98%, and the localization accuracy achieved sub-pixel level, demonstrating the capability of machine vision to solve road detection problems, indicating that this technical method is feasible. © 2025 SPIE.

关键词： Machine vision

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Research on surface disease perception using autonomous driving road radar detection

Research on surface disease perception using autonomous driv...

引用

2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Wei, Lili Zhou, Chunjiang Lin, Xingzhi GuangXi Vocational Normal University Xixiangtang District University East Road 105 GuangXi Nanning China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Xixiangtang District University East Road 105 GuangXi Nanning China

ISBN: (纸本)9781510690585

In response to the issues with surface disease perception technology, research was conducted on autonomous road radar detection of surface diseases. To address this problem, we approached it from two aspects: the road surface and the internal structure of the pavement, utilizing 3D laser point cloud scanners, 3D ground-penetrating radar, global navigation satellite systems, and high-precision positioning technology to achieve synchronous detection of surface and internal pavement diseases. The simulation results of radar detection for surface diseases indicate that the detection accuracy for surface diseases reaches99.61%, while the recognition accuracy for internal pavement diseases can reach94.0%, fully demonstrating the feasibility of the autonomous road radar detection technology for surface disease perception. © 2025 SPIE.

关键词： Global positioning system

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Deep learning-based automated driving vehicle dynamic trajectory planning and decision making 4

Deep learning-based automated driving vehicle dynamic trajec...

引用

4th International Conference on Computer technology, Information Engineering, and Electron Materials, CTIEEM 2024

作者： Deng, Xianglin Shi, Haochuang Lin, Xingzhi GuangXi Vocational Normal University Xixiangtang District University East Road 105 Guangxi Nanning China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Xixiangtang District University East Road 105 Guangxi Nanning China

ISBN: (纸本)9781510689299

Aiming at the technical problem of dynamic scene trajectory planning and decision-making for self-driving cars in the increasingly complex traffic and road environments, we have applied adaptive From the aspects of intelligent control and emergency scene change, interactive predictive path planning, complex traffic scene perception, etc., we use adaptive cruise control, automatic driving motion planning algorithm, convolutional neural network and other technological methods to realize the safe and efficient driving of the self-driving car in the complex and changeable as well as unfamiliar dynamic scenes. The application of intelligent networked vehicles to collaboratively verify the multi-class algorithms and driving scenarios shows that it is possible to realize the safe navigation of vehicles in complex traffic environments. © 2025 SPIE.

关键词： Convolutional neural networks

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Research on the Environmental Adaptation and Optimization of Unmanned Driving Road Detection vehicles with Ground Penetrating Radar 24

Research on the Environmental Adaptation and Optimization of...

引用

3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liang, Hanmei Lin, Tao Lin, Xingzhi GuangXi Vocational Normal University Guangxi Nanning China Guangxi Perception Internet of Things Productivity Promotion Center Guangxi Nanning China Guangxi Minzu University Guangxi Nanning China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Nanning China

ISBN: (纸本)9798400710834

In response to the optimization technology issues of geological radar for unmanned road detection vehicles, research was conducted on solutions regarding geological radar working modes, data processing and fusion, algorithm optimization, real-time feedback, and human-computer interaction. To address this issue, methods combining GPR technology with ultrasonic detection technology and GPR technology with geological resistivity detection technology were utilized or proposed, effectively analyzing the impact of soil temperature and environmental humidity on detection data accuracy. The environmental adaptive mechanism, data processing flow optimization, and algorithm adjustments in this study achieved a significant improvement in the accuracy of geological radar data under different soil and humidity conditions. The simulation experiment results indicate that through dynamic adjustments and environmental change compensation, the detection capability of geological radar has been enhanced by over 80%, demonstrating the ability to maintain efficiency and safety in complex geological environments, and the results indicate that this technical solution is feasible. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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Research on Wireless Dynamic Magnetic Resonance Charging technology for Autonomous vehicles 4

Research on Wireless Dynamic Magnetic Resonance Charging Tec...

引用

4th International Conference on Electronic Information Engineering and Computer technology, EIECT 2024

作者： Luo, Zhenhong Ou, Danfeng Lin, Tao Lin, Xingzhi GuangXi Vocational Normal University Nanning China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China Guangxi Perception Internet of Things Productivity Promotion Center Nanning China Guangxi Minzu University Nanning China

ISBN: (纸本)9798331528850

In response to the significant issues of low charging efficiency and insufficient system stability in autonomous vehicles during operation, a solution based on wireless dynamic magnetic resonance technology has been researched. To address this problem, methods for system architecture optimization, hardware design improvement, and control strategy adjustment have been proposed, effectively analyzing charging performance and system stability under different vehicle speeds and complex road conditions. The wireless dynamic magnetic resonance charging technology enhances charging efficiency, improves system stability, and extends driving range, achieving efficient energy transfer and smooth system operation during dynamic charging. Simulation results indicate that this solution demonstrates good adaptability under various operating conditions, leading to more stable system operation and the capability to solve the dynamic charging challenges of autonomous vehicles. The results show that this technological solution is feasible and has practical application potential. © 2024 IEEE.

关键词： Wireless charging

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Research on Environmental Perception Based on YOLOv8 and LiDAR Data Fusion 4

Research on Environmental Perception Based on YOLOv8 and LiD...

引用

4th IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Luo, ZhenHong Li, HuiLing Li, Ting Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350363586

To enhance the sensing capability of driverless cars in complex dynamic scenarios, a fusion method of radar and depth camera is emphasized and a technical framework for integrating multi-source sensors is proposed. A YOLOv8 neural network detection model combined with a self-attention network structure is first used to achieve correct recognition and depth estimation of vehicles and pedestrians, then further distance data of dynamic environment is acquired by M10P LiDAR, and finally information from D435i camera and radar information are fused by matrix projection. In the experiments, the conventional YOLOv8 model was compared to the YOLOv8 model enhanced with self-attention, the camera and LIDAR measurements were evaluated against the fusion results. The experimental results demonstrate that the new model significantly improves the confidence and stability of target detection, while the fused distance measurements are more accurate than those from individual sensors. © 2024 IEEE.

关键词： Sensor data fusion

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