Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors

Hou, Rui; Jeong, Seongwoon; Lynch, Jerome P.; Law, Kincho H.

doi:10.1016/j.trc.2019.11.024

Cited by 34 publications

(14 citation statements)

References 33 publications

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“…Cloud-based computing architecture for CPS data-driven device and process control emphasizes the need for scalable, performance-related methodologies and the use of predictive analysis for cloud-based master learning algorithms [25]. It is introducing a method for large-scale analytical data for radio-frequency identifying capable shop-floor transportation.…”

Section: Background To the Cyber-physical Systemmentioning

confidence: 99%

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Alsufyani

Alotaibi

Almagrabi

et al. 2021

Complex Intell. Syst.

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Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

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Section: Background To the Cyber-physical Systemmentioning

confidence: 99%

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Alsufyani

Alotaibi

Almagrabi

et al. 2021

Complex Intell. Syst.

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“…1.2 Literature review 1.2.1 Problems associated with deep learning In the field of perception and semantic understanding, deep learning (DL) is one of the mainstream technologies which has been used widely in practice. In transportation-related tasks, DL has been extensively adopted in applications including infrastructure management (Hou et al, 2020;Zhuang et al, 2018), traffic prediction (Cui et al, 2019;Liu et al, 2019;Yu et al, 2020a;Zhou et al, 2021), driver behavior modeling (Xing et al, 2021), smart routing systems (Du et al, 2021), smart intersection management (Peng et al, 2021) and traffic incident and duration recognition (Zhu et al, 2021). With respect to autonomous driving tasks, DL models have been applied in every submodule (Figure 1).…”

Section: Application Of Perception In Existing Driving Systemsmentioning

confidence: 99%

Development and testing of an image transformer for explainable autonomous driving systems

Dong

Chen

Miralinaghi

et al. 2022

JICV

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Purpose Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase. Design/methodology/approach This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features. Findings The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction. Originality/value In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

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“…The load could be detected through a fixed sensor, through the monitoring area, by a global vision, or a hybrid of these methods. The most related previous works with the development in this article are found in Catbas et al, 34 where an SHM method for bridges is proposed, based on the correlation of two vision-based systems (cameras), one for vehicle load detection and the other for bridge displacement, and Hou et al 35 integrated measurement of vehicular loads that create the responses measured during the SHM task (For a detailed review on vision-based systems for bridge monitoring, see Sony et al 36 ).…”

Section: Bridges Continuous Shm and Vbi-related Workmentioning

confidence: 99%

A structural health monitoring method proposal based on optical scanning and computational models

Flores-Fuentes

Alba-Corpus

Sergiyenko

et al. 2022

International Journal of Distributed Sensor Networks

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This article proposes a method for continuous bridge displacement monitoring combining the dynamic triangulation scanning and load estimation by vehicle image recognition. The vehicle–bridge interaction is a non-stationary dynamic process parameter of high relevance to understanding the static instability behavior of bridges. The knowledge of the load on a bridge in the specific time when its structural spatial coordinates are measured allows correlating the bridge displacement with the effect of vehicle–bridge interaction. The evaluation of such correlation is mandatory in order to verify if the observed bridge displacement is due to the nature of its operation or due to it is presenting structural damage. The proposed method is continuous structural health monitoring method, based on the combination of three approaches evaluated at laboratory environment: (1) a three-dimensional optical scanning system for displacement measurement, (2) a load measurement system for vehicle–bridge interaction assessment, and (3) a two-measurement systems data correlation; to be implemented in bridges at a real environment to collect their historical behavior. Overall, for each approach, the measurement systems’ principles, the laboratory experimental methodology followed, and results obtained are presented.

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Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors

Cited by 34 publications

References 33 publications

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Development and testing of an image transformer for explainable autonomous driving systems

A structural health monitoring method proposal based on optical scanning and computational models

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