2023Unpublished venueRequires access

Vehicles of Everything combined Yolov7 with Traffic enforcement camera on the roadside system

Wei-Ting Tai, Chien-Hao Pan, Khoa Van Pham, Yung-Yeh Lin, Chi‐Chia Sun

Open publisher page 1 citations

Abstract

This study proposes an intelligent roadside system based on a V2X vehicle network and intelligent traffic signals to address the important issues of traffic accidents and congestion in traffic management. With the V2X vehicle network, vehicles can obtain real-time information from traffic signals and use better methods to control their speed and direction, thus reducing the occurrence of accidents and congestion. In addition, the system uses CNN technology to improve detection performance under required conditions, and YOLOv7 technology can accurately identify vehicles. With these technologies, the intelligent roadside system can more sensitively detect vehicle behavior on the road, better coordinate traffic flow, improve road traffic efficiency, reduce traffic congestion, and also reduce the occurrence of traffic accidents. Overall, this intelligent roadside system based on a V2X vehicle network and intelligent traffic signals is expected to play an important role in urban traffic management, effectively reducing the occurrence of traffic accidents and congestion, improving road traffic efficiency, and providing a safer and more convenient driving experience for the public.

About this research paper

What this paper is about

This study proposes an intelligent roadside system based on a V2X vehicle network and intelligent traffic signals to address the important issues of traffic accidents and congestion in traffic management. With the V2X vehicle network, vehicles can obtain real-time information from traffic signals and use better methods to control their speed and direction, thus reducing the occurrence of accidents and congestion. In addition, the system uses CNN technology to improve detection performance under required conditions, and YOLOv7 technology can accurately identify vehicles. With these technologies, the intelligent roadside system can more sensitively detect vehicle behavior on the road, better coordinate traffic flow, improve road traffic efficiency, reduce traffic congestion, and also reduce the occurrence of traffic accidents. Overall, this intelligent roadside system based on a V2X vehicle network and intelligent traffic signals is expected to play an important role in urban traffic management, effectively reducing the occurrence of traffic accidents and congestion, improving road traffic efficiency, and providing a safer and more convenient driving experience for the public.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

This study proposes an intelligent roadside system based on a V2X vehicle network and intelligent traffic signals to address the important issues of traffic accidents and congestion in traffic management. With the V2X vehicle network, vehicles can obtain real-time information from traffic signals and use better methods to control their speed and direction, thus reducing the occurrence of accidents and congestion. In addition, the system uses CNN technology to improve detection performance under required conditions, and YOLOv7 technology can accurately identify vehicles. With these technologies, the intelligent roadside system can more sensitively detect vehicle behavior on the road, better coordinate traffic flow, improve road traffic efficiency, reduce traffic congestion, and also reduce the occurrence of traffic accidents. Overall, this intelligent roadside system based on a V2X vehicle network and intelligent traffic signals is expected to play an important role in urban traffic management, effectively reducing the occurrence of traffic accidents and congestion, improving road traffic efficiency, and providing a safer and more convenient driving experience for the public.

Key concepts: Floating car data, Vehicle Information and Communication System, Traffic congestion, Intelligent transportation system, Computer science, Traffic optimization, Traffic congestion reconstruction with Kerner's three-phase theory, SAFER

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