Review of Unmanned Aerial Vehicles (UAVs) Operation and Data Collection for Driving Behavior Analysis

Pham, Hai; Camey, M.; Pham, Khanh; Pham, Kristy; Rilett, Laurence R.

doi:10.1007/978-981-15-0802-8_178

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…UAV makes up for the shortcomings of satellites and manned aircraft, and collects images with higher temporal and spatial resolution to support forest inventory, health monitoring, silviculture and harvesting operations. Pham et al [17] focus on the application of UAV in traffic data collection. This paper not only compares various UAV operation frameworks and popular platforms, but also applies UAV in speed behavior analysis, gap acceptance and merging behavior.…”

Section: A Existing Surveys and Tutorialsmentioning

confidence: 99%

UAV-Assisted Data Collection for Internet of Things: A Survey

Wei

Zhu

Zhang

et al. 2022

IEEE Internet Things J.

163

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Thanks to the advantages of flexible deployment and high mobility, unmanned aerial vehicles (UAVs) have been widely applied in the areas of disaster management, agricultural plant protection, environment monitoring and so on. With the development of UAV and sensor technologies, UAV assisted data collection for Internet of Things (IoT) has attracted increasing attentions. In this article, the scenarios and key technologies of UAV assisted data collection are comprehensively reviewed. First, we present the system model including the network model and mathematical model of UAV assisted data collection for IoT. Then, we review the key technologies including clustering of sensors, UAV data collection mode as well as joint path planning and resource allocation. Finally, the open problems are discussed from the perspectives of efficient multiple access as well as joint sensing and data collection. This article hopefully provides some guidelines and insights for researchers in the area of UAV assisted data collection for IoT.

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Section: A Existing Surveys and Tutorialsmentioning

confidence: 99%

UAV-Assisted Data Collection for Internet of Things: A Survey

Wei

Zhu

Zhang

et al. 2022

IEEE Internet Things J.

163

View full text Add to dashboard Cite

show abstract

“…Reference [13] studied UAV-aided data collection from time-constrained sensors via jointly optimizing the trajectory of the UAV and the radio resource allocation. In [14], UAV operations and traffic data collection have been reviewed for driving behaviour analysis. In [15], energy-efficient data collection was studied, where sensor wake-up schedule and UAV trajectory were jointly optimized to minimize the maximum energy consumption for all sensors.…”

Section: B Related Workmentioning

confidence: 99%

Time Allocation and Optimization in UAV-Enabled Wireless Powered Communication Networks

Yan

Chen

Yang

2022

IEEE Trans. on Green Commun. Netw.

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“…Trajectory data sets have been used for different types of analysis, proving its the wide range of possibilities this kind of data provides. For example, it was investigated how speed behaviour changes in the inner-city tra c network taking multimodality into account (Paipuri et al 2021), microscopic parameters for driving behaviour were analysed (Pham et al 2020) and lane choice and lane change behaviour were considered (Espalader-Clapés et al 2023a). Tra c engineering applications have also been investigated, for example by estimating queue length pro les (Zhou et al 2021).…”

Section: Introductionmentioning

confidence: 99%

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Kutsch,

Margreiter,

Bogenberger

2024

Preprint

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Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behaviour of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 meters continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain the information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data therefore includes various interactions of different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and supporting data is available open source for research purposes to ensure global accessibility.

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Review of Unmanned Aerial Vehicles (UAVs) Operation and Data Collection for Driving Behavior Analysis

Cited by 14 publications

References 8 publications

UAV-Assisted Data Collection for Internet of Things: A Survey

UAV-Assisted Data Collection for Internet of Things: A Survey

Time Allocation and Optimization in UAV-Enabled Wireless Powered Communication Networks

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

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