• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.117-130
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• 2011

The purpose of this study is to produce a better way to detect crossroad accidents, which involves an efficient method to produce background images in consideration of object movement and preserve/demonstrate the candidate accident region. One of the prior studies proposed an employment of traffic signal interval within crossroad to detect accidents on crossroad, but it may cause a failure to detect unwanted accidents if any object is covered on an accident site. This study adopted inverse perspective mapping to control the scale of object, and proposed different ways such as producing robust background images enough to resist surrounding noise, generating candidate accident regions through information on object movement, and by using edge information to preserve and delete the candidate accident region. In order to measure the performance of proposed algorithm, a variety of traffic images were saved and used for experiment (e.g. recorded images on rush hours via DVR installed on crossroad, different accident images recorded in day and night rainy days, and recorded images including surrounding noise of lighting and shades). As a result, it was found that there were all 20 experiment cases of accident detected and actual effective rate of accident detection amounted to 76.9% on average. In addition, the image processing rate ranged from 10~14 frame/sec depending on the area of detection region. Thus, it is concluded that there will be no problem in real-time image processing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.2
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• pp.1-11
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• 1984

This aims of this study is to analyse the correlationship between traffic accident s and traffic characteristic variables in at-grade intersections of urban area, to build up an accident forecasting model and to propose an evaluation method of hazardous at-grade intersections. The accident forecasting model is formulated by the use of residual indexes that is selected by principal component analysis and its statistical significance is tested by step-wise regression analysis. Effective countermeasures for safety can be established on the basis of identifying high accident intersections, because the validity of this model was examined and found to coincide with real world situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.222-234
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• 2020

Autonomous vehicles are equipped with a wide rage of sensors such as GPS, RADAR, LIDAR, camera, IMU, etc. and are driven by recognizing and judging various transportation systems at intersections in the city. The accident ratio of the intersection of the autonomous vehicles is 88% of all accidents due to the limitation of prediction and judgment of an area outside the sensing distance. Not only research on non-signalized intersection collision avoidance strategies through V2V and V2I is underway, but also research on safe intersection driving in failure situations is underway, but verification and fragments through simple intersection scenarios Only typical V2V failures are presented. In this paper, we analyzed the architecture of the V2V module, analyzed the causal factors for each V2V module, and defined the failure mode. We presented intersection scenarios for various road conditions and traffic volumes. we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to analyze the risk of autonomous vehicle based on the simulation. We presented ASIL, which is the result of risk analysis, proposed a monitoring concept for each component of the V2V module, and presented monitoring coverage.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• Transportation Technology and Policy
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• v.12 no.5
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• pp.34-42
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• 2015

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.339-347
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• 2007

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.199-211
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• 2008

This study deals with the side right-angle collisions of 4-legged signalized intersections in Cheongju. The goals are to analyze the characteristics of accidents and to find out the accident factors that affect severity using ordered probit model. In pursuing the above, the study uses the data of 580 side right-angle collisions occurred at the 181 intersections(2004-2005). The analyses show that more accidents were occurred in the nighttime and in going straight. The main cause was analyzed to be the red-light violation. Also, the main results of modeling are the following, First, the likelihood ratio index is 0.094 and t-ratio values that explain goodness of fit are significant. Second, minor road traffic volumes, minor road lanes, major road left-turn lanes, major road left-turn signal, major road yellow signal time, cross angle, major and minor road speed limits are significant factors affecting crash severities at signalized intersections.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.437-439
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• 2006

Actually, The present traffic accident detection system is subsisting limitation of accurate distinction under the crowded condition at intersection because the system defend upon mainly the image information at intersection and digital image processing techniques nearly all. To complement this insufficiency, this article aims to estimate the level of present technology and a realistic possibility by analyzing the acoustic characteristic of crash sound that we have to investigate for improvement of traffic accident detection rate at intersection. The skid sound of traffic accident is showed the special pattern at 1[kHz])${\sim}$3[kHz] bandwidth when vehicles are almost never operated in and around intersection. Also, the frequency bandwidth of vehicle crash sound is showed sound pressure difference oyer 30[dB] higher than when there is no occurrence of traffic accident below 500[Hz].

• Proceedings of the ESK Conference
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• 1998.04a
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• pp.105-110
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• 1998

운전자의 인지 능력을 충분히 고려하지 않은 도로 교통 표지판은 운전 상황에서 순간적인 의사 결정을 어력베 하여 교통사고의 가능성을 증가 시킬 수 있으므로 이에 대한 개선책이 시급하다 하겠다. 그러므로 본 연구에서는 수도권 도로 교통 표지판의 인지적 문제를 설문 조사와 현장 조사를 통하여 구체적으로 분 석하고 이에따른 개선안을 제시하였다. 이를 위해, 서울 시내의 사고 다발 지역을 중심으로 교차로 표지 판의 인지공학적 결함을 면밀히 분석하였고, 1997년 표지판 개정안의 설계 원칙에 대한 인지적 문제점을 도출하였다. 그 중, 교차로의 방향 표지, 방향 유도 표지에 나타난 정보 처리 과정의 문제점 및 노면 표지 의 설계가 운전자의 인지적 혼돈을 유발시킬 수 있다는 가정하에 이에 대한 모의 실험을 실시 하였다. 24명 의 피설험자를 대상으로, 개선된 표지판과 현 표지판을 모의 운전 상황에 따라 간헐적으로 컴퓨터 화면을 통해 관측하게 한 후 인지된 내용의 정확도와 반응 시간을 비교하여 분석하였다. 그 결과에 따라 기존 노면 표지의 형태를 개선하고, 방향 예고 표지판과 방향 표지판의 인지적 구별을 명확하게 하는 것이 운전자에게는 정확하고 신속한 의사 결정을 하게 한다는 것을 발견 하고 이에 대한 원칙을 제시하였다. 결론적으로 표지판의 설계와 설치에 있어 행정적인 지원과 인지적인 요인에 대한 고려가 된다면 표지판의 오독으로 인한 교통 사고를 줄여 나가는데 기여할 것으로 기대된다.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.289-294
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• 2008