A Survey of Safety Warnings Under Connected Vehicle Environments

Li, Haijian; Zhao, Guoqiang; Qin, Lingqiao; Aizeke, Hanimaiti; Zhao, Xiaohua; Yang, Yanfang

doi:10.1109/tits.2020.3026309

Cited by 18 publications

(8 citation statements)

References 84 publications

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“…We are experiencing the urbanization and the proliferation of various vehicles. It has been a hot topic to implement highly-efficient intelligent transportation systems (ITS) and develop autonomous vehicles (AVs), consequently realizing smart transportation [68]- [72]. Meanwhile, diverse traffic sensors, Global Navigation Satellite System (GNSS), radar, loop detectors, light detection and ranging (LIDAR), and Infrared (IR) cameras deployed at ITS and AVS also generate/collect massive diverse types of traffic data, which can be used to analyze the traffic status and identify possible faults in transportation systems.…”

Section: Iiot-enabled Smart Transportationmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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Section: Iiot-enabled Smart Transportationmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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“…Some researchers have focused on developing systems for AVs that are human‐centered, can detect driving errors, and activate autonomous driving to prevent accidents [108]. Other researchers have focused on developing systems that issue safety warnings in real time [109, 110]. [109] reviewed the state of research on safety warnings for vehicle environments that are under connected.…”

Section: Metaverse In Data‐driven Intelligent Transportation Systemsmentioning

confidence: 99%

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

Njoku

Nwakanma

Amaizu

et al. 2022

IET Intelligent Trans Sys

132

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The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

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“…Thus, transportation safety has been a crucial issue in smart transportation. Sending safety warnings to vehicles can inform drivers beforehand so as to avoid collisions [91]. During this process, real-time and reliable V2X communications can guarantee the timely delivery of warning messages.…”

Section: A Requirements and Architecturementioning

confidence: 99%