UAV monitoring and forecasting model in intelligent traffic oriented applications

Liu, Jingyu; Wu, Jing; Li, Mingyu

doi:10.1016/j.comcom.2020.02.009

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…Since a UAV is restricted by flight time, it is paramount to determine the priority between transmission range and bit rate in a specific LoRa application. The time duration for the transmission of sensor data can also be increased to achieve a low bit rate and stable connectivity [45]. However, it should be fast enough for the user to react accordingly, especially in monitoring applications.…”

Section: Transmission Powermentioning

confidence: 99%

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A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

2021

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The term Internet of Things (IoT) has emerged in recent decades because this network revolutionizes almost every aspect of our daily life, including products such as smartphones and intelligent vehicles, and crucial tasks such as precision agriculture and environmental monitoring. Myriads of communication technologies have been developed to fulfill the two main features of the IoT: long-range transmissions and low power consumption. Long-range (LoRa) has become one of the vital parts of IoT communication. In this study, the real-time deployments of an unmanned aerial vehicle (UAV)-based LoRa communication network are systematically reviewed, with a focus on the communication setup and its reported performance. Importantly, the UAV-based LoRa communication network has a low bit rate connectivity to ensure the high reliability of connections, especially in applications that require long transmission ranges. This study provides recommendations for researchers on what research perspectives need to be explored when implementing UAVs for IoT-based LoRa communication. This study also describes publication trends related to UAV-based LoRa communication networks. A supplementary Excel file that contains the reported publications on UAV-based LoRa communication networks is included to show this publication trend.

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Section: Transmission Powermentioning

confidence: 99%

“…A UAV-based LoRa gateway is typically used for data collection from ground LoRa nodes [43], [45], [62]- [66] and to increase the communication ranges [29], [67]. 8 summarizes the backhaul communication modules integrated by the researchers in the LoRa network applications.…”

Section: B Uav-based Lora Gatewaymentioning

confidence: 99%

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

2021

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“…With these advantages, UAV-driven aerial sensing systems have been widely explored in traffic monitoring for applications such as safety analysis, traffic condition estimation, and driving behavior recognition. For safety-related monitoring, UAVs are used for traffic accident detection [17,18], disaster forecasting or prevention [19,20] and crash risk estimation [21]. Liu et al (2017) applied image mosaic technology road traffic accident detection using UAV images [17].…”

Section: Introductionmentioning

confidence: 99%

Extracting Key Traffic Parameters from UAV Video with On-Board Vehicle Data Validation

Shan

Tian

Yin

et al. 2021

Sensors

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The advantages of UAV video in flexibility, traceability, easy-operation, and abundant information make it a popular and powerful aerial tool applied in traffic monitoring in recent years. This paper proposed a systematic approach to detect and track vehicles based on the YOLO v3 model and the deep SORT algorithm for further extracting key traffic parameters. A field experiment was implemented to provide data for model training and validation to ensure the accuracy of the proposed approach. In the experiment, 5400 frame images and 1192 speed points were collected from two test vehicles equipped with high-precision GNSS-RTK and onboard OBD after completion of seven experimental groups with a different height (150 m to 500 m) and operating speed (40 km/h to 90 km/h). The results indicate that the proposed approach exhibits strong robustness and reliability, due to the 90.88% accuracy of object detection and 98.9% precision of tracking vehicle. Moreover, the absolute and relative error of extracted speed falls within ±3 km/h and 2%, respectively. The overall accuracy of the extracted parameters reaches up to 98%.

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“…Unmanned Aerial Vehicle (UAV), as a kind of modern equipment with strong mobility and excellent autonomous control capability, has received extensive attention from scholars in various research fields. Its research and application have been involved in post disaster medical assistance, intelligent transportation, agricultural science, location monitoring and other modern industries (Yao et al, 2019;Liu et al, 2020;Radoglou-Grammatikis et al, 2020;Sarıçiçek & Akkuş, 2015). In the research of UAV application, the problem of UAV flight path planning is the benchmark problem for all applications and is the precondition to ensure that UAV can service better.…”

Section: Introductionmentioning

confidence: 99%

Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

Yang

Bi³

et al. 2021

Preprint

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Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

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UAV monitoring and forecasting model in intelligent traffic oriented applications

Cited by 19 publications

References 17 publications

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

A Systematic Review of Real-Time Deployments of UAV-Based LoRa Communication Network

Extracting Key Traffic Parameters from UAV Video with On-Board Vehicle Data Validation

Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

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