• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.127-136
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• 2012

Multi-car rear-end collision accidents have three categories: sequential collision from the rear which is commonly referred to as chain reaction collision, sequential collision from the front, and mixed-order collision. This paper suggests several effective methods of reconstruction analysis for multi-car rear-end collision accidents. First, by incorporating the traditional empirical method which uses vehicle damage caused by brake dive and passenger injuries, with results of theoretical analysis made within mechanics of rigid body, it is made possible for the method to be put to immediate practical use. A methodology to precisely analyze multi-car rear-end collision accidents was suggested using a simulation program simultaneously with a video event data recorder which is starting to be widely used in domestic vehicles. To go beyond the simple intuitive analysis of the video event data recorder, the simulation analysis based on the results of video analysis was executed to acquire various information, so that the causes and responsibility could be clearly stated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.303-310
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• 2018

One of most shocking headlines is a serious chain collision accident (CCA). The development of CCA has a temporal and spatial locality, and the information of the CCA is time-critical. Due to these characteristics of CCA, traffic accident information should be rapidly propagated to drivers in order to reduce chain collisions, right after the first accident occurs. Inter-vehicle communication (IVC) based on ad-hoc communication is one of promising alternatives for locally urgent information propagation. Despite this potential of IVC, research for the effects of IVC on the reduction of CCA has not been reported so far. Therefore, this study develops the parallel platform of microscopic vehicle and IVC communication simulators and then analyses the effects of IVC on the reduction of the second collision related to a series of vehicles. To demonstrate the potential of the IVC-based propagation of urgent traffic accident information for the reduction of CCA, the reduction of approaching-vehicle speed, the propagation speed of accident information, and then the reduction of CCA were analysed, respectively, according to scenarios of combination of market rates and traffic volumes. The analysis results showed that CCA can be effectively reduced to 40~60% and 80~82% at the penetration rates of 10% and 50%, respectively.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.179-184
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• 2012

A car accident on a highway occurs a chain-reaction collision because of a vehicle's fast velocity. In order to prevent it, the accident vehicle should broadcast a safe message to its neighbors. If there are many neighbor nodes, a frame collision probability is high. To solve this, it was proposed for a system as a previous study to send a safe message without frame-collision using separating channels. However, the separation of multiple channels make feasibility low because of increasing hardware's development cost and complexity. In this paper, we proposes a TDMA-based MAC protocol using a single channel. As a result, we show the frame reception success rate of our protocol was almost the same as the previous protocol.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.295-300
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• 2012

Various forward collision warning algorithms have studied in order to protect a car accident. For this, in general, algorithms using an external device such as a camera and sensor generate a forward collision warning. However, if using the external device, it can occur errors due to device characteristics when there is rain or fog. Also, the prevention of a chain-reaction collision is insufficient because the system generates a warning in case of only vehicle having a forward collision danger. If it combines the vehicle safety communications, the method becomes a solution to protect a chain-reaction collision. So, In this paper, we proposes a improved forward collision warning algorithm using the wireless communication technique, driver's information, breaking distance, and velocity. And we compare and analyze our algorithm and previous algorithms.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.319-324
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• 2012

Vehicle safety communications is one among the important technologies in order to protect a car accident. For this, many protocols forwarding a safe message have studied to protect a chain-reaction collision when a car accident occurs. most of these protocols assume that the time of generating a safe message is the same as an accident's. If a node predicts some traffic hazard and forwards a safe message, a driver can response some action quickly. So, In this paper, we proposes a traffic hazard prediction algorithm using the communication technique. As a result, we show that the frame reception success rate of using our algorithm to the previous protocol improved about 4~5%.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.276-281
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• 2014

Necessities of a traffic means work a car in the modern society human to an usability of a life is enjoying. On the other hand, the damage by traffic accident increment the human quotient worked as we were in proportion to the vehicle which increased. Passing an examination moves necessarily on an obstacle to suddenly appear at the fronts if a car travels and the vehicles which stopped suddenly. Dynamic passing an examination about an obstacle turn on Vehicle Emergency Lamp to by hand when is unhurried, and can turn off, but to appear urgently dynamic passing an examination in time human is instinctive, but cannot inform an emergency to a back vehicle, and a rear-end collision occurs. A car we synthesize a speed of a vehicle, and this unit analyzes as we use GPS, and to drive runs Vehicle Emergency Lamp to automatic in the situations that shall turn on emergencies etc. If a speed of a vehicle continuously slows down in too high-speed driving or low-speed driving, or we are stopped, Vehicle Emergency Lamp is always turned on. It was built if we rise again as clearing itself from risk, and a speed of a vehicle judges, and we turn off Vehicle Emergency Lamp to automatic. It runs till rear-end collision sensor operates, and by hand reset does Vehicle Emergency Lamp a driving vehicle collides from behind to a back vehicle or when a driving vehicle was overthrown. It is shortened very much to the chain rear-end collision traffic accident that is a traffic accident of large size if we use this unit. And we did authentication through the experiment which a driver was helpful to unnecessary operation and a relaxed safe driving during drivings.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.161-166
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• 2012

When a car accident happened on a highway, the accident vehicle should broadcast a safe message to its neighbors in order to prevent a chain-reaction collision. Also, there is a problem that the estimation accuracy is low because of the memory limit from increasing the sampling count. In this paper, we proposes a HVC systems using a back-off algorithm applied to a gaussian model. And we proposes a MAC protocol preventing the communication delay by separating the neighbor count collection channel, data channel, and RSU communication channel. As a result, we show the frame reception success rate of our protocol improved about 10% than the previous protocol.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1164-1167
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• 2021

동절기, 야간 등 도로에서 결빙으로 인한 연쇄 추돌 사고는 교통 체증 및 2차 사고의 위험으로 이어진다. 도로 중 결빙 발생 다발 지역인 지방도로, 터널 출입구, 교량 구간, 산기슭 도로, 그늘진 곡선 도로를 대상으로 C-ITS 관점 안전운전 결빙 주의보 애플리케이션을 제공하여 결빙으로 발생하는 사고를 미리 예방하고자 한다. 노면/기상 상태를 아두이노, 기상 api로 측정, 차량 운전자용 앱(GIS/맵 기반) 구현을 통해 앱 사용 운전자 간 양방향 V2V, 운전자와 아두이노 센서 간 V2I 통신으로 결빙으로부터 운전자를 보호함에 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.