A Survey of Communications for Intelligent Transportation Systems

Naja, Rola

doi:10.1007/978-1-4419-9563-6_1

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…ITS is pivotal in augmenting AVs' capabilities as they rely on up-to-date traffic parameters to navigate effectively [45], [192]. Moreover, ITS facilitates V2V [46] and V2I communication [193], [194], enabling seamless interaction between vehicles and various elements of the ITS. This becomes especially vital when, for instance, an AV needs to negotiate a complex intersection, requiring awareness of nearby vehicles and traffic signals.…”

Section: DL For Autonomous Vehicles In Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

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Section: DL For Autonomous Vehicles In Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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show abstract

“…In particular, ITS can help to enhance the transportation infrastructures, its overall safety, and security of critical information for different transportation means. It must be noted that currently, the main focus of academics and industry is on vehicular networks and more precisely on developing inter-vehicle and vehicle-to-infrastructure networks [1][2][3]. However, ITS are not limited only to manage vehicular traffic but they can also provide services and they can be successfully implemented in air, water transport, and rail systems [4,5] as well.…”

Section: Introductionmentioning

confidence: 99%

Network anomaly detection for railway critical infrastructure based on autoregressive fractional integrated moving average

Andrysiak

Saganowski

Mazurczyk

2016

J Wireless Com Network

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The article proposes a novel two-stage network traffic anomaly detection method for the railway transportation critical infrastructure monitored using wireless sensor networks (WSN). The first step of the proposed solution is to find and eliminate any outlying observations in the analyzed parameters of the WSN traffic using a simple and fast one-dimensional quartile criterion. In the second step, the remaining data is used to estimate autoregressive fractional integrated moving average (ARFIMA) statistical models describing variability of the tested WSN parameters. The paper also introduces an effective method for the ARFIMA model parameters estimation and identification using Haslett and Raftery estimator and Hyndman and Khandakar technique. The choice of the "economically" parameterized form of the model was based on the compromise between the conciseness of representation and the estimation of the error size. To detect anomalous behavior, i.e., a potential network attack, the proposed detection method uses statistical relations between the estimated traffic model and its actual variability. The obtained experimental results prove the effectiveness of the presented approach and aptness of selection of the statistical models.

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Federated learning analysis for vehicular traffic flow prediction: evaluation of learning algorithms and aggregation approaches

Nidhi,

Grover

2024

Cluster Comput

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A Survey of Communications for Intelligent Transportation Systems

Cited by 9 publications

References 19 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Network anomaly detection for railway critical infrastructure based on autoregressive fractional integrated moving average

Federated learning analysis for vehicular traffic flow prediction: evaluation of learning algorithms and aggregation approaches

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