• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.1
• /
• pp.91-105
• /
• 2024

Car accidents occur as a result of collisions between vehicles, leading to both vehicle damage and personal and material losses. This study developed a vehicle accident detection model based on 2,550 image frames extracted from car accident videos uploaded to YouTube, captured by CCTV. To preprocess the data, bounding boxes were annotated using roboflow.com, and the dataset was augmented by flipping images at various angles. The You Only Look Once version 8 (YOLOv8) model was employed for training, achieving an average accuracy of 0.954 in accident detection. The proposed model holds practical significance by facilitating prompt alarm transmission in emergency situations. Furthermore, it contributes to the research on developing an effective and efficient mechanism for vehicle accident detection, which can be utilized on devices like smartphones. Future research aims to refine the detection capabilities by integrating additional data including sound.

• Journal of Platform Technology
• /
• v.9 no.4
• /
• pp.90-96
• /
• 2021

Most of the vehicles currently under development and in operation are equipped with various IoT sensors, but some of the factors that cause car accidents are relatively difficult to detect. One of the major risk factors among these factors is black ice. Black ice is one of the factors most likely to cause major accidents, as it can affect all vehicles passing through areas covered with black ice. Therefore, black ice detection technique is essential to prevent major accidents. For this purpose, some studies have been carried out in the past, but unrealistic factors have been reflected in some parts, so research to supplement this is needed. In this paper, we tried to detect black ice by analyzing color images using the CNN technique, and we succeeded in detecting black ice to a certain level. However, there were differences from previous studies, and the reason was analyzed.

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
• /
• pp.273-275
• /
• 2011

최근 교통량이 증가함에 따라 자동차 사고피해도 비례하여 증가하고 있으며, 이로 인해 CCTV 등과 같이 교통사고 예방에 소모되는 비용이 막대하게 지출되고 있다. 단일시점 카메라의 시스템은 객체들의 겹침, 카메라각도에 의한 인식오류 등으로 오차율이 높은 단점이 있다. 이를 보완하기 위해 다중시점의 협업기반 자동 상황인지 시스템을 제안한다. 제안하는 방법은먼저 영상데이터로부터 차량, 사람 등의 객체를 추출하고 이들 객체 쌍의 특징 정보를 계산한다. 이를 바탕으로 각 카메라 센서노드의 규칙기반 시스템을 이용하여 객체간의 사고여부를 가려낸다. 각 센서노드의 사고여부 정보는 메인서버로 수집되고, 수집된 정보는 상위 규칙에 의해 최종 사고 여부가 판단된다. 본 논문에서는 실제 교차로에 설치된세대의 카메라를 이용한 실험을 통해 제안하는 시스템의 성능을 검증하였다.

• 한국HCI학회:학술대회논문집
• /
• 2007.02b
• /
• pp.608-611
• /
• 2007

본 연구는 가상환경에서 위반행동을 한 사람에게 그 행동에 관한 단서를 제시하였을 때 나타나는 심리 생리적 변화를 측정하고자 한다. 피험자는 가상환경에서 주어진 시나리오에 따라 목적지까지 자동차를 운전하는 역할을 수행한다. 시나리오는 운전을 하는 도중에 사고를 내고 차량을 수리하는 내용으로 구성하였다. 피험자는 위반, 관찰, 통제집단의 세 집단으로 선별되며, 위반집단은 고의적으로 교통사고를 일으키게 된다. 위반 행동을 한 피험자에게 사건에 관련된 질문을 컴퓨터로 제시하고 동시에 뇌파를 측정한다. 사건 관련 질문은 위반행동과 관련이 있는 장소, 차량, 행동에 대한 답변보기들로 구성되었으며 피험자에게는 위반행동과 무관한 보기답안과 함께 제시된다. 측정결과, 위반행동과 무관한 보기답안 보다 위반행동과 관련된 보기답안에서 높은 뇌파 반응이 나타났다. 따라서 이를 이용하여 위반행동을 탐지하는 것이 가능할 것으로 보인다. 연구 확장을 통해 가상환경으로 실제 위반 상황을 재구성하여 적용하는 것도 유용해 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.524-526
• /
• 2021

As the invention of automobiles and construction of roads for vehicles began, the occurrence of traffic accidents began to increase. Accordingly, efforts were made to prevent traffic accidents by changing the road construction method and using signal systems such as traffic lights, but until now, numerous human and property damages have occurred every year due to traffic accidents caused by freezing of the road due to bad weather. In this paper, we propose a method of transmitting ice detection data detected using vehicle sensor data to vehicle navigation to reduce traffic accidents caused by road freezing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.521-523
• /
• 2021

As the invention of automobiles and construction of roads for vehicles began, the occurrence of traffic accidents began to increase. Accordingly, efforts were made to prevent traffic accidents by changing the road construction method and using signal systems such as traffic lights, but even today, numerous human and property damages have occurred due to traffic accidents caused by freezing of the road due to bad weather. In this paper, in order to reduce traffic accidents due to road freezing, we propose a method of transferring the ice detection information obtained by deep learning of infrared wavelength data obtained using an infrared camera to the vehicle's navigation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.416-421
• /
• 2021

In case of the road slips due to heavy snow and the temperature drops below 0 degrees, black ice which mainly occurs on the road, bridges for vehicles, and tunnel entrances, is not recognized by the driver's view because the image of the asphalt is transmitted through it. So cars' slip situation occurs, which leads to a big traffic accident and a large amount of loss of life and property. This study proposes a method to check the road condition using an infrared camera and to identify black ice through deep learning.

• The Journal of Bigdata
• /
• v.7 no.1
• /
• pp.15-27
• /
• 2022

In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.9
• /
• pp.308-313
• /
• 2016

Modern systems have become more and more complex due to the ever-increasing user requirements and rapid advance of technology. As such, the frequency of accidents due to system design errors or failure has been increasing. When the damage incurred by accidents to human beings or property is serious, the underlying systems are referred to as safety-critical systems. The development of such systems requires special efforts to ensure the safety of the human beings operating them. To cope with such a requirement, in this paper an approach is employed in which we consider safety starting from the conceptual design phase of the systems. Specifically, a systems design method that can detect functional failure is proposed by utilizing meta-models and M&S methods. To accomplish this, the safety design data from international safety standards are first extracted and also a meta-model is generated using SysML (systems modeling language). Then, a SysML-based system design method is proposed based on the use of the developed meta-model. We also discuss how the safety requirements can be created and verified using a simulation method. Finally, through a case study in automotive design, it is demonstrated that the detection of a functional failure and the verification of a safety requirement can be accomplished using the SysML-based M&S method. This study indicates that the use of meta-models can be useful for collecting and managing safety data and that the meta-model based M&S method can make it possible to satisfy the system requirements by reducing the design errors.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.150-156
• /
• 2011

Smart Highway is a next generation highway that significantly improves a traffic safety, reduces incidence of traffic accidents, and supports intelligent and convenient driving environments so that drivers can drive at high speeds in safety. In order to implement smart highway, it is required to gather a large amount of data including conditions of a road and the status of vehicles, and other useful data. To provide situation information of highway, it has been gathered traffic information using optical sensors(CCTV, etc.). However, this technique has problems such as the problem of information gathering, lack of accuracy depending on weather conditions and limitation of maintenance. It needs radar system which has not effect on environmental change and algorithm processing technique in order to provide information for a safety driving to driver and car. In this paper, it is used radar with 9.4GHz to test performance of a road surface and developed radar system for detecting test. And we compared and analyzed a performance of data acquired from each radar through computer simulation.