Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion

Li, Yanbing; Zhang, Weichuan

doi:10.1038/s41598-023-27696-z

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Multiple regression models such as sexual models were also used to evaluate the data cleaning results of the system [18]. Abdulhakim et al established a FAHES system that can detect data and fill in missing values.…”

Section: Data Cleaningmentioning

confidence: 99%

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

Yan

2024

IJCSIT

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As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.

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Section: Data Cleaningmentioning

confidence: 99%

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

Yan

2024

IJCSIT

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“…Digital twin technology is an effective way to achieve the information management of mountain roads [6][7][8][9][10]. Digital twin technology is the mapping of the properties, structure, state, performance and behaviour of real physical entities to the virtual world [11][12][13][14][15][16][17][18]. Digital twin technology also seeks to create a realistic virtual scene of mountain highways with high fidelity and interconnection of all elements, realise the accurate representation of highway operation status and rapid response to road condition information, and better support the information management decision of mountain highways [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

Tang,

Zhu,

Ren

et al. 2023

IJGI

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Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.

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“…He et al [14] presented an in-depth study on autonomous anomaly detection in traffic flow data using reinforcement learning and an LSTM model, learning anomaly patterns without supervision and automating the detection process. Furthermore, Li and Zhang [15] published a paper on generating a digital twin (DT) model of traffic flow for highway scenarios using radar and camera fusion, proposing an end-to-end method involving sensor calibration, data transformation, and target tracking using a fusion Kalman filter. The DT model could provide real-time dynamic traffic flow data to optimize traffic efficiency and enhance the functionality of ITS [15].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Li and Zhang [15] published a paper on generating a digital twin (DT) model of traffic flow for highway scenarios using radar and camera fusion, proposing an end-to-end method involving sensor calibration, data transformation, and target tracking using a fusion Kalman filter. The DT model could provide real-time dynamic traffic flow data to optimize traffic efficiency and enhance the functionality of ITS [15].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

Haghshenas,

Astarita,

Guido

et al. 2023

JCCE

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Traffic flow analysis is an interesting study topic in transportation studies. A better understanding of traffic flow is essential for more effective traffic reduction methods. Because managing traffic flow in cities is getting more complicated, we need more methodical ways to deal with these problems. Machine learning techniques have been suggested as a possible solution because they can process great amounts of data and give insights that can be used to help make decisions about how to manage traffic. The main objective of this research is to conduct a comprehensive examination of the quantitative and qualitative aspects of utilizing machine learning techniques in the management of traffic flow. Using the Web of Science (WOS) platform, documents from January 2007 to April 2023 were assessed. The study found that traffic flow management has been using machine learning techniques more and more over the past few years. This study shows the different approaches and methods that were used, as well as the results and limits of these methods. The results recommend that machine learning can be a useful tool for managing traffic flow in cities, but further investigation is warranted to gain a complete comprehension of both the advantages and disadvantages of the subject under scrutiny.

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Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion

Cited by 10 publications

References 36 publications

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

Assessment of Machine Learning Techniques and Traffic Flow: A Qualitative and Quantitative Analysis

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