• 한국도로학회:학술대회논문집
• /
• 2003.10a
• /
• pp.327-332
• /
• 2003

• Journal of the Ergonomics Society of Korea
• /
• v.35 no.3
• /
• pp.135-142
• /
• 2016

Objective: Objective of this study is to provide characteristics of injury frequency and severity by driving condition in large truck-related traffic collisions. Background: Traffic accidents involving large trucks draw a lot of attention in accident prevention and management policies since they bring about severe human and financial damages. Method: In order to identify the major risk factors of accidents by driving condition, 255 recognized traffic accidents by large truck drivers were analyzed in terms of time of the day, road type, and shape of the road. Results: The driving conditions in the results are represented by the following form of combination, "Road Type (Non-expressway or Express) - Shape of Roads (Straight, Curved, Downhill, or Intersection) - Time of Accidents (Day or Night)". In the analysis of injury frequency, Non-expressway-Straight-Day condition was the most frequent one. Meanwhile, Expressway-Curved-Day, Non-expressway-Curved-Night and Non-expressway-Intersection-Night were evaluated as high level in view of injury severity. Also, Expressway-Straight-Night is the driving condition that is the highest in risk among the conditions that have to be managed as grade "High". Non-expressway-Straight-Night, Non-expressway-Downhill-Day, and Non-expressway-Curved-Day are also categorized as grade "High". Conclusion and Application: Safety managers in the fields require basic information on accident prevention that can be easily understood. The research findings will serve as a practical guideline for establishing preventive measures for traffic accidents.

• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.53-59
• /
• 2022

After the 3rd level autonomous driving vehicle, the 4th and 5th level of autonomous driving technology is trying to maintain the optimal condition of the passengers as well as the perfect driving of the vehicle. However current autonomous driving technology is too dependent on visual information such as LiDAR and front camera, so it is difficult to fully autonomously drive on roads other than designated roads. Therefore this paper proposes a Braking Strength Calculation System (BSCS), in which a vehicle classifies road conditions using data other than visual information and calculates optimal braking strength according to road conditions and driving conditions. The BSCS consists of RCDM (Road Condition Definition Module), which classifies road conditions based on KNN algorithm, and BSCM (Braking Strength Calculation Module), which calculates optimal braking strength while driving based on current driving conditions and road conditions. As a result of the experiment in this paper, it was possible to find the most suitable number of Ks for the KNN algorithm, and it was proved that the RCDM proposed in this paper is more accurate than the unsupervised K-means algorithm. By using not only visual information but also vibration data applied to the suspension, the BSCS of the paper can make the braking of autonomous vehicles smoother in various environments where visual information is limited.

• International Journal of Highway Engineering
• /
• v.13 no.3
• /
• pp.121-129
• /
• 2011

The purpose of this study is to determine the relationship between observed traffic data and NOx concentrations from not an ideal condition but a real road in real-time. Also we aim to develop an estimation model for NOx emission concentrations due to vehicle exhaust gas, and it can be applied to monitor the degree of air pollution on National Road in real-time. To eliminate outliers which are occurred due to errors of equipments and other variables, we use the robust analysis and develop two models. which are considering and not considering wind impact. The result of this research can be used for understanding present condition of air pollution caused by vehicle exhaust gas and evaluating for environmental effects of transportation policy.

• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.745-757
• /
• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.11a
• /
• pp.654-655
• /
• 2008

In order to investigate the availability of tire airborne noise for vehicle road noise development, We measured the noise in condition of smooth road and coarse road. The correlation coefficient was analyzed using the articulation index of the tire airborne noise and the vehicle road noise. It has been found that the correlation between the tire airborne noise and the vehicle road noise is positively strong.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2010.05a
• /
• pp.391-392
• /
• 2010

The frozen road is effected as major cause of car accident in winter. Especially, road surface freezing on the highway can lead to fatal accident. The accident by frozen road can effectively reduced by prevent road surface freezing before it frozen as evaluate road surface condition. Therefore, this study installed thermometer in each depth of concrete pavement for evaluate road surface conditions which freezing chronically. The result of this study will be used as preliminary data for predict before freezing.

• Journal of the Korean Institute of Educational Facilities
• /
• v.10 no.4
• /
• pp.31-41
• /
• 2003

The purpose of this study is to analyze the Characteristic of Block Plans related to site types of Elementary Schools in Kangju city. With analysis of 17 elementary school plans, we reached same conclusions. First, there is a possibility of seeing 4 examples that contiguity road condition and proportion of site, neighborhood park location, district units planning, which as classification the condition to feature of location. But the condition which is considered actually from Block Plan appeared with only contiguity road condition and district units planning two onlines. Second, despite there are elements having the possibility effecting to Block Plan, some arrangement forms are repeatedly used. There is a possibility of searching for that cause that it falls in mannerism of space form, type and place when it follows the arrangement form of existing defined as school.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.18 no.1
• /
• pp.8-13
• /
• 2010

Many vehicles have significantly under-inflated tires, primarily because drivers infrequently check their vehicles' tire pressure. When a tire is used while significantly under-inflated, its sidewalls flex more and the tire temperature increases, increasing stress and the risk of failure. In this study we evaluated tire safety and economical efficiency at various inflation pressure. For tire safety we performed FMVSS indoor durability test, measurement of rolling tire temperature, braking performance at dry/wet road condition, and rolling resistance test for economical efficiency. Results show that low pressure decreases tire durability of both speed-increase condition and load-increase condition. Heat temperature of rolling tire increases as pressure decreases and significantly under-inflated tires cause increase of vehicle's stopping distance at wet road condition. Also Under-inflation increases the rolling resistance of a tire and, correspondingly, decreases vehicle's fuel economy.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.221-224
• /
• 2007

MMS(Mobile Mapping System) generates a efficient image data for mapping and facility management. However, this image data of MMS has many difficulties in a practical use because of huge data volume. Therefore the important information likes road sign post must be extracted from huge MMS image data. In Korea, there is the HMS(Highway Management System) to manage a national road that acquire the line and condition of road from the MMS images. In the HMS each road sign information is manually inputted by the keyboard from moving MMS. This manually passive input way generate the error like inaccurate position, mistaking input in this research we developed the automatic road sign identifying technique using the image processing and the direct geo-referencing by GPS/INS data. This development brings not only good flexibility for field operations, also efficient data processing in HMS.