When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

Zhou, Xuan; Ke, Ruimin; Yang, Hao; Liu, Chenxi

doi:10.3390/app11209680

Cited by 28 publications

(7 citation statements)

References 158 publications

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“…The method could achieve good performance in several evaluation metrics. The limitations and possible improvements of our study were: (1) In terms of the text corpus describing pedestrian crossing scenes, the problem of insufficient text information leads to the construction of a small text repository, which may affect the accuracy of the model in extracting triplets and thus the quality of the knowledge graph construction, so the scale of the text repository could be expanded in subsequent studies through crawling techniques and data enhancement. (2) In the pedestrian intention estimation mechanism, the feature factors considered by the BN model were mainly selected based on the text information of the text library corresponding to the PSI dataset.…”

Section: Discussionmentioning

confidence: 99%

“…The interest in autonomous driving technology as an essential part of intelligent transportation systems is rising [1]. With the development of deep learning technology in recent years, autonomous driving technology is rapidly developing, and various companies have invested much energy in researching self‐driving vehicles [2], such as Waymo and Uber.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Zhou,

Bai,

et al. 2023

IET Intelligent Trans Sys

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With the development of deep learning technology, the problem of data‐driven trajectory prediction and intention recognition has been widely studied. However, the pedestrian trajectory prediction and intention recognition methods based solely on data‐driven have weak data description ability and black‐box characteristics, which cannot reason about pedestrian crossing intention and predict pedestrian crossing trajectory as humans do. To address the above problems, the authors proposed a data and knowledge‐driven pedestrian intention estimation and trajectory prediction method by imitating human cognitive mechanisms. In the pedestrian intention inference process, the authors adopted the knowledge‐driven method. As a first step, the authors built a knowledge graph of pedestrian crossing scenes, and then paired it with a Bayesian network to estimate pedestrian crossing intentions. In the pedestrian trajectory prediction process, the authors used a data‐driven approach, combining pedestrian crossing trajectory features and knowledge‐based pedestrian intentions. Experiments show that all evaluation metrics of pedestrian trajectory prediction were improved after adding pedestrian intentions obtained by knowledge‐driven.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Zhou,

Bai,

et al. 2023

IET Intelligent Trans Sys

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“…The large amount of data generated from the increasing variety of sensing technologies introduces difficulties for the TMC to process and fully utilize the data for decision-making. The relatively long delay makes TMC-based services struggle to meet the ultra-fast response time requirements of many advanced ITS applications, such as connected and autonomous vehicles, real-time traffic surveillance and warning, short-term traffic prediction, and so forth ( 5 , 17 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Cooperative and Comprehensive Multi-Task Surveillance Sensing and Interaction System Empowered by Edge Artificial Intelligence

Liu

Yang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Numerous sensors were introduced to intelligent transportation systems (ITS) in the past decade. Consequently, new sensing technologies and the generated data attracted more and more attention, which brought new challenges, such as redundant sensors, huge maintenance costs, and data explosion, to ITS. To satisfy the core demands of traffic agencies in a more effective and efficient way, therefore, this paper proposes the idea of “Sensing as a Service” (SaaS) and implements it in the Cooperative and Comprehensive Smart Edge Node for Sensing and OpeRation (COCO SENSOR) system for practical deployments. COCO SENSOR is an innovative multi-task sensing system, which was developed to address key practical applications, including real-time vehicle counting and recognition, road-surface-condition classification, visibility estimation, and live communication among traffic controllers and road users in a single unit. COCO SENSOR introduced customized cooperative sensing and a parallel computation mechanism to increase perception accuracy and efficiency with limited computation resources on the edge device. In collaboration with the Washington State Department of Transportation and the City of Bellevue, a field experiment was conducted to test the system’s performance. The results of the experiment show that the COCO SENSOR effectively fills the gap between sensing and traffic services and that it successfully executed four practical applications, including traffic-volume counting by vehicle type, traffic-status detection, road-visibility estimation, and road-surface-condition classification, with high accuracy. Additionally, a mobile app was developed for both traffic managers and users to access comprehensive traffic information and live warning messages.

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“…), and can be considered a main preprocessing stage (see Figure 18 ). We stress that an application of the proposed DAT representation of the image provides the following: Resource-efficient EC and training at the edge, which is of particular importance, for example, in intelligent transportation systems [ 57 ]; Easy implementation in fog EC and mobile EC architectures [ 18 ], as well as in “non-classic” ones, for instance, short supply circuit IoT [ 58 ]; Data protection, in particular, satisfies the so-called zero-trust principle, which belongs to the set of top trends [ 40 ]; a high level of protection and confidentiality is ensured by the great variety of settings, in particular, the atomic function applied in DAT and a structure of this core procedure, as well as several ways to encode quantized DAT coefficients; despite this, its comparison to other methods, for example, biometric security through visual encryption [ 59 ] and lightweight cryptographic algorithm [ 60 ], must be carried out; Ability to construct artificial intelligence of things or AIoT systems [ 20 ], as well as that one which provides distributed learning, edge learning, and mobile intelligence [ 44 ]. …”

Section: Edge Computing-based Application Of Dacmentioning

confidence: 99%

“…Resource-efficient EC and training at the edge, which is of particular importance, for example, in intelligent transportation systems [ 57 ];…”

Section: Edge Computing-based Application Of Dacmentioning

confidence: 99%

Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems

Makarichev

Illiashenko

Kharchenko

2022

Sensors

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Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed.

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When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

Cited by 28 publications

References 158 publications

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Pedestrian intention estimation and trajectory prediction based on data and knowledge‐driven method

Cooperative and Comprehensive Multi-Task Surveillance Sensing and Interaction System Empowered by Edge Artificial Intelligence

Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems

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