Smart Infrastructure: A Research Junction

Hetzel, Manuel W; Reichert, Hannes; Doll, Konrad; Sick, Bernhard

doi:10.1109/isc253183.2021.9562809

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…A detailed description of the research intersection can be found in [32]. The research intersection is located in the inner-city of Aschaffenburg and is highly frequented by VRUs and vehicles.…”

Section: A Research Intersectionmentioning

confidence: 99%

The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset

Hetzel

Reichert

Reitberger

et al. 2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the perception capabilities of drivers and vehicle perception systems. Additionally, they offer the opportunity to gather reproducible and precise data of a holistic scene understanding, including context information as a basis for training algorithms for various applications in automated traffic. Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context Dataset (IMPTC). We use an intelligent public inner-city intersection in Germany with visual sensor technology. A multi-view camera and LiDAR system perceives traffic situations and road users' behavior. Additional sensors monitor contextual information like weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multiagent interaction. The resulting dataset consists of eight hours of measurement data. It contains over 2,500 VRU trajectories, including pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and over 20,000 vehicle trajectories at different day times, weather conditions, and seasons. In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration-and detection data until trajectory and context data. The dataset is continuously expanded and is available online for non-commercial research at https://github.com/kav-institute/imptc-dataset.

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Section: A Research Intersectionmentioning

confidence: 99%

The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset

Hetzel

Reichert

Reitberger

et al. 2023

2023 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

View full text Add to dashboard Cite

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Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework

Hakimi,

Liu,

Abudayyeh

et al. 2023

Buildings

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Effective civil infrastructure management necessitates the utilization of timely data across the entire asset lifecycle for condition assessment and predictive maintenance. A notable gap in current predictive maintenance practices is the reliance on single-source data instead of heterogeneous data, decreasing data accuracy, reliability, adaptability, and further effectiveness of engineering decision-making. Data fusion is thus demanded to transform low-dimensional decisions from individual sensors into high-dimensional ones for decision optimization. In this context, digital twin (DT) technology is set to revolutionize the civil infrastructure industry by facilitating real-time data processing and informed decision-making. However, data-driven smart civil infrastructure management using DT is not yet achieved, especially in terms of data fusion. This paper aims to establish a conceptual framework for harnessing DT technology with data fusion to ensure the efficiency of civil infrastructures throughout their lifecycle. To achieve this objective, a systematic review of 105 papers was conducted to thematically analyze data fusion approaches and DT frameworks for civil infrastructure management, including their applications, core DT technologies, and challenges. Several gaps are identified, such as the difficulty in data integration due to data heterogeneity, seamless interoperability, difficulties associated with data quality, maintaining the semantic features of big data, technological limitations, and complexities with algorithm selection. Given these challenges, this research proposed a framework emphasizing multilayer data fusion, the integration of open building information modeling (openBIM) and geographic information system (GIS) for immersive visualization and stakeholder engagement, and the adoption of extended industry foundation classes (IFC) for data integration throughout the asset lifecycle.

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Smart Infrastructure: A Research Junction

Cited by 2 publications

References 10 publications

The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset

The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset

Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework

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