Design of AI Vehicle Cut-in Prevention System to Improve Traffic Jam 


Vol. 48,  No. 12, pp. 1689-1695, Dec.  2023
10.7840/kics.2023.48.12.1689


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  Abstract

Traffic jam is a social problem that not only consumes excessive time and money, but also negatively affects safety and environment. Traffic jams are caused by the increase in vehicles and the driving habits of drivers in restrictive road conditions. In particular, a vehicle intervening near the end of an access road with heavy vehicle traffic gives stress to drivers who have waited for a long time and causes an accident. In this paper, we study a method to prevent cut-in, which is the fundamental cause of traffic jam. Cameras and radars are installed on entrances and exits where traffic jams frequently occur, and traffic jams are checked through learning based on the captured image data. Using the cut-in prevention system in case of traffic jam, the LED variable lane is changed from a dotted line to a solid line. Through this, cutting-in of vehicles is prevented, and vehicles violating cutting-in are detected with license plates and administrative measures are taken. This study aims to reduce traffic jam and create a safe driving culture by improving drivers' driving habits.

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  Cite this article

[IEEE Style]

B. Yang and D. Park, "Design of AI Vehicle Cut-in Prevention System to Improve Traffic Jam," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 12, pp. 1689-1695, 2023. DOI: 10.7840/kics.2023.48.12.1689.

[ACM Style]

Bee-seul Yang and Dea-woo Park. 2023. Design of AI Vehicle Cut-in Prevention System to Improve Traffic Jam. The Journal of Korean Institute of Communications and Information Sciences, 48, 12, (2023), 1689-1695. DOI: 10.7840/kics.2023.48.12.1689.

[KICS Style]

Bee-seul Yang and Dea-woo Park, "Design of AI Vehicle Cut-in Prevention System to Improve Traffic Jam," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 12, pp. 1689-1695, 12. 2023. (https://doi.org/10.7840/kics.2023.48.12.1689)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880