• Journal of Korea Multimedia Society
• /
• v.10 no.2
• /
• pp.209-217
• /
• 2007

This paper proposed to a detection scheme of weather information that is a part of CCTV Images Weather Detection System using CCTV images and Temperature, Humidity. The previous Partial Weather Detection System uses how to acquire weather information using images on the Road. In the system the contrast and RGB Values using clear images are gained. This information is distributed a input images to cloud, rain, snow and fog images. That is, this information is compared the snow and the fog images for acquisition more correctness information us ing difference images and binary images. Currently, We use to environment sense system, but we suggest a new Weather Detection Algorithm to detect weather information using CCTV images. Our algorithm is designed simply and systematically to detect and separate special characteristics of images from CCTV images. and using temperature & humidity in formation. This algorithm, there is more complex to implement than how to use DB with high overhead of time and space in the previous system. But our algorithm can be implement with low cost' and can be use the system in real work right away. Also, our algorithm can detect the exact information of weather with adding in formation including temperature, humidity, date, and time. At last, this paper s how the usefulness of our algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.1
• /
• pp.216-238
• /
• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.43 no.9 s.351
• /
• pp.142-148
• /
• 2006

Considering the domestic traffic environment and the increase of traffic accidents, we have been asked to exactly analyze the main causes of accidents for the accident-experienced drivers to be rehabilitated. In this thesis we present the development process and results of a driving simulator using the IPDE method in the interest of safe driving and driving rehabilitation. Through this Driving simulation development the rehabilitated driver has the possibility of experiencing the real driving situation with the driving aptitude and examines the reasons of accidents. Through the examinations the driver has the chance to correct the deformities of driving by choosing the explanatory scenes, and through this process the driver is able to develop the capability to react in the real situation. However this driving simulation system is one of the best developed, depending on weather and road condition the braking distance may change. Therefore the fuzzy rule and neural network have been used in this thesis to solve previously mentioned problem. The simulation exactly calculated the road and weather conditions to adjust the breaking intensity.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.10 no.5
• /
• pp.16-20
• /
• 1973

The most important thing in locating disturbances in optimal rapid traffic singnal control is to collect information cocerning toraffit flow by means of a detection method. In order to set up an optimal traffic singnal period, the analysis of a delay time phenomena in the signal period must also be considered. In fact, each of the distributed traffic quantities on the road are not similar factors in view of speeds and distances of succeeding cars. The causing factors are analyzed by the method of control engineering analysis, and they are coincident with disturbance. Thus distubances cause errors. Distubances are fuctions of time, and are classified into three conditions: Natural road state and weather are the first. The second is structures and function of vehicles, and the third is inducedbydrivers. This thesis deals with the last two cases except the first one for maximum utilization of the existing road state and weather conditions. The first condition remains constant, and then there exist some relations between vehicles and drivers. In the long run, it can be shown that the scheme for minimizing whole errors in the optimal traffic signal time setting is definitely presented.

• Journal of the Society of Disaster Information
• /
• v.20 no.1
• /
• pp.138-146
• /
• 2024

Purpose: The purpose of this study is to find cases of using information and communication technology and smart mobility technology in snow removal vehicles and equipment for rapid and efficient road snow removal in the event of a snowstorm, and to find ways to utilize them. Method: Cases of domestic and overseas snow removal methods are investigated, and snow removal operation methods incorporating new technologies are presented. Result: Most of the operation of snow removal equipment in Korea uses GPS, CCTV, and road traffic information systems, and in the case of overseas, road weather information systems and road snow removal monitoring systems are used. It is expected that snow removal technology using autonomous snow removal vehicles, which are smart mobility, will be developed in the future. Conclusion: The results of this study can contribute to the policy of using snow removal equipment and snow removal vehicles of local governments and related organizations.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.3
• /
• pp.223-230
• /
• 2021

Currently, self-driving vehicles are on the verge of commercialization after testing. However, even though autonomous vehicles have not been fully commercialized, 81 accidents have occurred, and the driving method of vehicles to avoid accidents relies heavily on LiDAR. In order for the currently commercialized 3-level autonomous vehicle to develop into a 4-level autonomous vehicle, more information must be collected than previously collected information. Therefore, this paper proposes a Driving Situation Judgment System (DSJS) that accurately calculates the crisis situation the vehicle is in by useing the roughness of the road and the state of the passengers of surrounding vehicles including road information and weather information collected from existing autonomous vehicles. As a result of DSJS's PDM experiment, PDM was able to classify passengers 15.52% more accurately on average than the existing vehicle's passenger recognition system. This study can be a basic research to achieve the 4th level autonomous vehicle by collecting more various types than the data collected by the existing 3rd level autonomous vehicle.

• 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.

• Journal of Korean Society for Atmospheric Environment
• /
• v.34 no.5
• /
• pp.735-747
• /
• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Journal of Convergence for Information Technology
• /
• v.11 no.7
• /
• pp.14-20
• /
• 2021

Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.