Automated techniques for real time platoon detection and identification

Sudheer, Sidharth; Thomas, Helen; Sharma, Anuj; Vanajakshi, Lelitha

doi:10.1109/itsc.2016.7795805

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…A time headway threshold value of 3 s was used to distinguish between the free and non-free moving behaviors of vehicles on two-lane roads. Sudheer et al [47] proposed automated techniques for detecting platoons in heterogeneous traffic through the utilization of three distinct methodologies: a cluster-based approach, a modified Gaur and Mirchandani approach, and an image processing-based technique.…”

Section: Research Backgroundmentioning

confidence: 99%

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Kasi,

Karuppanan

2024

Sustainability

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Vehicle platoon studies are essential for understanding and managing traffic on urban arterial roads. The identification of vehicle platoons on urban roads has drawn more attention in recent years. Researchers have been exploring various methods and algorithms to detect and classify platoons, as well as investigating the benefits and implications of their presence on road capacity, safety, fuel consumption, and environmental pollution. The present study formulated a three-step strategy to identify vehicle platoons in the urban road network under heterogeneous traffic conditions. The proposed three steps are recognizing vehicle interaction, the estimation of critical headway, and vehicle platoon identification. Traffic data were collected for 13 h in a six-lane divided urban arterial road using an infrared sensor. A Python program was developed to recognize vehicle platoons. The results revealed that out of a total of 42,500 vehicles observed, 74% of vehicles were in vehicle platoons. The characteristics of the identified vehicle platoons were studied, thus focusing on key aspects such as platoon size, intra-platoon headway, platoon stream speed, and vehicle composition in the platoon. The results revealed a linear relationship between the percentage of vehicles in the platoon and traffic volume. The findings of the study will be beneficial in examining platoon-based data aggregation, the utilization of road capacity, and traffic flow optimization.

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Section: Research Backgroundmentioning

confidence: 99%

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Kasi,

Karuppanan

2024

Sustainability

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Level-5 Autonomous Driving—Are We There Yet? A Review of Research Literature

et al. 2022

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Autonomous vehicles are revolutionizing transport and next-generation autonomous mobility. Such vehicles are promising to increase road safety, improve traffic efficiency, reduce vehicle emission, and improve mobility. However, for these vehicles to live up to their full potentials, there are significant research, technological and urgent organizational issues that need to be addressed to reach the highest level of automation, i.e., level 5. Sensors, communication, mobile edge computing, machine learning, data analytic, distributed learning, and so on, are examples of the building blocks technologies and concepts constituting the end-to-end solution. This survey discusses these technologies and concepts and maps their roles to the end-to-end solution. It highlights the challenges for each technology. Moreover, this survey provides an analysis of different solution approaches proposed by relevant stakeholders, utilizing these technologies aiming to achieve level-5 autonomy. Finally, the article details two use cases to present the interplay of the building blocks technologies.

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Automated techniques for real time platoon detection and identification

Cited by 2 publications

References 8 publications

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Level-5 Autonomous Driving—Are We There Yet? A Review of Research Literature

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