Realtime Estimation of IEEE 802.11p for Mobile working Machines Communication respecting Delay and Packet Loss

Xiang, Yusheng; Su, Tianqing; Brach, Christine; Liu, Xiaole; Geimer, Marcus

doi:10.1109/iv47402.2020.9304651

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…As shown in Fig. 10, 2 regions have more conflicts than others, i.e., the region including vertex (8,6) and vertex (9,6), and the region including vertex (6,10). Same as the method, namely construction site optimization, used in the previous case, the vertex (5,10) is indicated, which shall be modified from an obstacle to a road with good conditions.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Same as the method, namely construction site optimization, used in the previous case, the vertex (5,10) is indicated, which shall be modified from an obstacle to a road with good conditions. In addition, the road condition of the vertex (9,5) and vertex (10,6) shall be changed into good condition from bad condition. After this optimization, the computation time is greatly reduced to roughly 0.17 seconds.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Also, Tumer describes the future construction site utilizing industry 4.0 [33]. Without a doubt, a future working site should be fully benefited from AI [34]- [36], automation technology [37], Simultaneous Localization and Mapping (SLAM) [38], and IoT [10], [11], [39], [40]. Therefore, the uncertainty degree of construction sites is reducing, and thus we consider the construction sites as known in our research.…”

Section: B Path Planning For Construction Machinesmentioning

confidence: 99%

“…Because the start points and endpoints can be given directly in the BIM system, we devote ourselves to the implementation level, i.e., how exactly the machines should achieve their goal set given by the BIM system. We envision that the case in Wuhan will be a normal case in the future by utilizing AI and IoT [10], [11] technology.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Xiang¹,

Liu

Su³

et al. 2021

IEEE Access

Self Cite

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Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms' performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet's productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm's most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Experiments Resultsmentioning

confidence: 99%

Section: B Path Planning For Construction Machinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Xiang¹,

Liu

Su³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…To achieve the smart working site, effective communication among mobile machines is an inevitable vital step. Since the mobile machines are very likely to work at a place where there is outside of the coverage of the base station, we utilized the ad-hoc network as the first version for the fleet management of the mobile machines [16]. In that paper, although the realtime communication system is proposed, the bidirectional communication between human and mobile construction machines is still a gap.…”

Section: E Wireless Connection Of Mobile Construction Machinesmentioning

confidence: 99%

Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Xiang¹,

Tang

et al. 2021

IEEE Access

Self Cite

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The CRDNN is a combined neural network that can increase the holistic efficiency of torque based mobile working machines by about 9% by means of accurately detecting the truck loading cycles. On the one hand, it is a robust but offline learning algorithm so that it is more accurate and much quicker than the previous methods. However, on the other hand, its accuracy can not always be guaranteed because of the diversity of the mobile machines industry and the nature of the offline method. To address the problem, we utilize the transfer learning algorithm and the Internet of Things (IoT) technology. Concretely, the CRDNN is first trained by computer and then saved in the on-board ECU. In case that the pre-trained CRDNN is not suitable for the new machine, the operator can label some new data by our App connected to the on-board ECU of that machine through Bluetooth. With the newly labeled data, we can directly further train the pretrained CRDNN on the ECU without overloading since transfer learning requires less computation effort than training the networks from scratch. In our paper, we prove this idea and show that CRDNN is always competent, with the help of transfer learning and IoT technology by field experiment, even the new machine may have a different distribution. Also, we compared the performance of other SOTA multivariate time series algorithms on predicting the working state of the mobile machines, which denotes that the CRDNNs are still the most suitable solution. As a by-product, we build up a human-machine communication system to label the dataset, which can be operated by engineers without knowledge about Artificial Intelligence (AI).

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Methodology to Quantitatively Assess Impacts of 5G Telecommunications Cybersecurity Risk Scenarios on Dependent Connected Urban Transportation Systems

Vargas

Tien

2022

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Realtime Estimation of IEEE 802.11p for Mobile working Machines Communication respecting Delay and Packet Loss

Cited by 9 publications

References 19 publications

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Fast CRDNN: Towards on Site Training of Mobile Construction Machines

Methodology to Quantitatively Assess Impacts of 5G Telecommunications Cybersecurity Risk Scenarios on Dependent Connected Urban Transportation Systems

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