An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System

Chen, Chen; Liu, Bin; Wan, Shaohua; Qiao, Peng; Pei, Qingqi

doi:10.1109/tits.2020.3025687

Cited by 311 publications

(138 citation statements)

References 51 publications

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“…After application of specific algorithms, these sensors can be easily used for almost full traffic surveillance, which involves vehicle classification, estimation of vehicle speed or traffic jam detection, eventually as a part of specific sensor systems [ 26 ]. After the implementation of appropriate software, magnetometer sensors could also provide a value-added contribution into the area of traffic surveillance, where other technologies take the role, as for example in sensing drunken drivers [ 27 ], as a part of driving assistance systems [ 28 ], for the edge traffic flow detection [ 29 ], as energy-efficient substitution of cameras for the detection and classification of road vehicles [ 30 ], or for the traffic abnormality detection [ 31 ] etc.…”

Section: Related Workmentioning

confidence: 99%

Which Digital-Output MEMS Magnetometer Meets the Requirements of Modern Road Traffic Survey?

Hodoň

Karpis

Ševčı́k

et al. 2021

Sensors

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Present systems for road traffic surveillance largely utilize MEMS magnetometers for the purpose of vehicle detection and classification. Magnetoresistive sensing or LR oscillation circuitry are technologies providing the sensors with the competitive advantage which lies in the energy efficiency and low price. There are several chip suppliers on the market who specialize in the development of these sensors. The aim of this paper is to compare available sensors from the viewpoint of their suitability for traffic measurements. A summary of the achieved results is given in the form of the score for each sensor. The introduced sensor chart should provide the audience with knowledge about pros and cons of sensors, especially if intended for the purposes of road traffic surveillance. The authors in this research focused on the specific situation of road traffic monitoring with magnetometers placed at the roadside.

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Section: Related Workmentioning

confidence: 99%

Which Digital-Output MEMS Magnetometer Meets the Requirements of Modern Road Traffic Survey?

Hodoň

Karpis

Ševčı́k

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…CEC is a crucial technique for SIoT based on edge computing, which provides connections for users with low latency, high bandwidth, and high reliability [8], [9]. For instance, it can support high-quality communications for vehicles to implement unmanned driving [10], [11] and intelligent transportation system [12], [13]. However, CEC migrates the users' private information to network edge from data centers.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Nie

Wang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users' privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-inthe-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method. Index Terms-Collaborative edge computing (CEC), generative adversarial network (GAN), intrusion detection, social internet of things (SIoT).

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“…To improve traffic problems and build smart cities, the intelligent transportation system (ITS) has been widely studied [5,6]. Especially in recent years, with the rapid development of 5G communication technology, artificial intelligence, sensor technology, and high-performance chip technology, the related technology of ITS has exploded, such as intelligent connected vehicles (ICVs) [7], vehicle-to-everything (V2X) [8,9], and edge sensing and computing [10,11]. Over-thehorizon perception for ICVs based on V2X and intelligent roadside units (RSUs) can be used for collision prevention at intersections to improve traffic safety [12,13].…”

Section: Introductionmentioning

confidence: 99%

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Bai

Zhang

et al. 2021

Sensors

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Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

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An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System

Cited by 311 publications

References 51 publications

Which Digital-Output MEMS Magnetometer Meets the Requirements of Modern Road Traffic Survey?

Which Digital-Output MEMS Magnetometer Meets the Requirements of Modern Road Traffic Survey?

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

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