Vision-Based Multi-detection and Tracking of Vehicles Using the Convolutional Neural Network Model YOLO

Moaga, Mpho; Chunling, Tu; Owolawi, Pius

doi:10.1007/978-3-031-47724-9_34

Lecture Notes in Networks and Systems

2024

DOI: 10.1007/978-3-031-47724-9_34

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Vision-Based Multi-detection and Tracking of Vehicles Using the Convolutional Neural Network Model YOLO

Mpho Moaga,

Tu Chunling,

Pius Owolawi

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Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Ušinskis,

Makulavičius,

Petkevičius

et al. 2024

Sensors

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Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented.

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Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Ušinskis,

Makulavičius,

Petkevičius

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Vision-Based Multi-detection and Tracking of Vehicles Using the Convolutional Neural Network Model YOLO

Cited by 1 publication

References 21 publications

Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

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