• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.683-686
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• 2018

Autonomous vehicles are the 4th industrial revolution that utilizes artificial intelligence(AI) and superconducting technology, and is a world-wide investment and research project. However, a Uber vehicle under test in Arizona, USA, was accidentally killed by pedestrians crossing the road in the dark night, and accidents occurred when the Tesla vehicle was exposedto the backlightof the sun. These problems were caused by misunderstandings and choice about sensors mounted on autonomous vehicles due to bad weather such as snow, rain, and sunlight. In this paper, we analyze the composition of the autonomous vehicle and the cause of the accident, and consider the criteria that should be judged in case of emergency in which human accidents may occur. This paper analyzes the composition of autonomous vehicles and causes of accidents, and considers the criteria that should be choice in an emergency where an accident may occur.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.3
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• pp.255-261
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• 2010

Forensic engineering is the area of expertise of those qualified to serve as engineering experts in courts of law or arbitration proceedings. Especially failure of internal parts during vehicle fire accidents causing injuries and damage to property almost always generates conflict between the automaker and customer. Hence, the investigation of such events generally involves an engineering analysis. One of the possible reasons for such accidents caused by vehicle fire is the failure of the piston and connecting rod. By formal inspections and engineering tests, this study shows the results of investigation and the cause of failure of the mechanical parts. For this purpose the failure mechanism is analyzed by using fractography methods and by applying an instrumented indentation technique to compare the material properties of the reference part with those of the malfunctioning part.

• Journal of Trauma and Injury
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• v.34 no.4
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• pp.263-269
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• 2021

Purpose: There have been increasing concerns about serious traffic accidents on highways. The purpose of this study was to analyze factors affecting traffic accidents on highways and the severity of the resulting injuries. Methods: This retrospective study was conducted at a regional trauma center. We reviewed 594 patients who had been in 114 traffic accidents on highways from January 2018 to June 2020. We collected demographic data, clinical data, accident-related factors, and meteorological data (weather and temperature). Results: Environmental risk factors were found to be significantly associated with the incidence of traffic accidents on highways. Injury severity and the death rate were higher in sedans than in any other type of vehicle. Tunnels were the most common location of accidents, accounting for 47 accidents (41.2%) and 269 injured patients (45.3%). The injury severity of individuals riding in the driver's seat (front seat) was high, regardless of vehicle type. Three meteorological risk factors were found to be significantly associated with traffic accidents: rainy roads (odds ratio [OR] 2.08; 95% confidence interval [CI] 1.84-3.29; p=0.01), icy or snowy roads (OR 5.12; 95% CI 2.88-7.33; p<0.01), and foggy conditions (OR 2.94; 95% CI 2.15-4.03; p<0.05). Conclusions: The injury severity of patients was affected by seat position and type of vehicle, and the frequency of accident was affected by the location. The incidence of traffic accidents was strongly influenced by meteorological conditions (rain, snow/ice, and fog).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1248-1254
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• 2022

Efforts are being made to prevent traffic accidents in the school zone in advance. However, traffic accidents in school zones continue to occur. If the driver can know the situation information in the child protection area in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. It is designed by improving the LIDAR system that recognizes vehicle speed and pedestrians. It collects and processes pedestrian and vehicle image information recognized by cameras and LIDAR, and applies artificial intelligence time series analysis and artificial intelligence algorithms. The artificial intelligence traffic accident prevention system learned by deep learning proposed in this paper provides a forced push service that delivers school zone information to the driver to the mobile device in the vehicle before entering the school zone. In addition, school zone traffic information is provided as an alarm on the LED signboard.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.209-214
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• 2023

In the future, as autonomous vehicles become popular at home and abroad, the frequency of accidents involving autonomous vehicles is also expected to increase. In particular, when a fully autonomous vehicle is operated, various criminal/civil problems such as sexual violence, assault, and fraud between passengers may occur as well as the vehicle accident itself. In this case, forensics for accidents involving autonomous vehicles and accidents involving passengers in the vehicles are also about to change. This paper reviewed the types of security threats of autonomous vehicles, methods for maintaining the integrity of evidence data using blockchain technology, and research on digital forensics. Through this, it was possible to describe threats that would occur in autonomous vehicles using blockchain technology and forensic techniques for each type of accident in a scenario-type manner. Through this study, a block that helps forensics of self-driving vehicles before and after accidents by investigating forensic security technology of domestic and foreign websites to respond to vulnerabilities and attacks of autonomous vehicles, and research on block chain security of research institutes and information security companies. A chain method was proposed.

• Knowledge Management Research
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• v.25 no.1
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• pp.217-233
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• 2024

The demand for two-wheelers has increased in recent years, driven by the growing delivery culture, which has also led to a rise in the number of two-wheelers. Although two-wheelers are economically efficient in congested traffic conditions, reckless driving and ambiguous traffic laws for two-wheelers have turned two-wheeler accidents into a significant social issue. Given the high fatality rate associated with two-wheelers, the severity and risk of two-wheeler accidents are considerable. It is, therefore, crucial to thoroughly understand the characteristics of two-wheeler accidents by analyzing their attributes. In this study, the characteristics of two-wheeled vehicle accidents were categorized using the K-prototypes algorithm, based on data from two-wheeled vehicle accidents. As a result, the accidents were divided into four clusters according to their characteristics. Each cluster showed distinct traits in terms of the roads where accidents occurred, the major laws violated, the types of accidents, and the times of accident occurrences. By tailoring enforcement methods and regulations to the specific characteristics of each type of accident, we can reduce the incidence of accidents involving two-wheelers in metropolitan areas, thereby enhancing road safety. Furthermore, by applying machine learning techniques to urban transportation and safety, this study adds to the body of related literature.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.195-203
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• 2009

This research wished to risk type and examine closely driver special quality and relation of traffic accidents by occurrence type of traffic accidents and traffic accidents seriousness examine closely relation with Severity. Fractionate traffic accidents type by eight, and driver's special quality for risk group's classification did to distinction of sex, vehicle type, age etc. analyzed relation with injury degree adding belt used putting on availability for security the objectivity with wave. Used log-Linear model and Logit model for analysis of category data. A head-on collision and overtaking accident, right-turn accident are high injury or death accident and possibility to associate in relation with accident type and seriousness degree. In risk group analysis The age less than 20 years in motor-cycle driver, taxi driver in 41 years to 50 years old are very dangerous. The woman also was construed to the more risk group than man from when related to car, mini-bus, goods vehicle etc. Therefore, traffic safety education and Enforcement for risk group that way that can reduce accident that produce to reduce a loss of lives at traffic accidents appearance a head-on collision and overtaking accidents, right-turn accidents should be studied and as traffic accidents weakness class may have to be solidified.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.173-188
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• 2024

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

• Journal of Auto-vehicle Safety Association
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• v.10 no.3
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• pp.20-26
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• 2018

It was a pilot study for developing an algorithm to determine the presence or absence of cervical spine injury by analyzing the severity factor of the patients in motor vehicle occupant accidents. From August 2012 to October 2016, we used the KIDAS database, called as Korean In-Depth Accident Study database, collected from three regional emergency centers. We analyzed the general characteristics with several factors. Moreover, cervical spine injury patients were divided into two groups: Group 1 for from Quebec Task Force (hereinafter 'QTF') grade 0 to 1, and group 2 for from QTF grade 2 to 4. The score was assigned according to the distribution ratio of cervical spine injured patients compared to the total injured patients, and the cut-off value was derived from the total score by summation of the assigned score of each factors. 987 patients (53.0%) had no cervical spine injuries and 874 patients (47.0%) had cervical spine injuries. QTF grade 2 was found in 171 patients (9.2%) with musculoskeletal pain, QTF grade 3 was found in 38 patients (2.0%) with spinal cord injuries, and QTF grade 4 was found in 119 patients (6.4%) with dislocation or fracture, respectively. We selected the statistically significant factors, which could be affected the cervical spine injury, like the collision direction, the seating position, the deformation extent, the vehicle type and the frontal airbag deployment. Total score, summation of the assigned each factors, 10 was presented as a cut-off value to determine the cervical spine injury. In this study, it was meaningful as a pilot study to develop algorithms by selecting limited influence factors and proposing cut-off value to determine cervical spine injury. However, since the number of data samples was too small, additional data collection and influencing factor analysis should be performed to develop a more delicate algorithm.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.794-801
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• 2021

Purpose: The effectiveness of the transport vehicle management system of the Chemical Substances Control Act will be studies and used as basic data for future system improvement plans. Method: After the enforcement of the Chemical Substances Control Act, the effectiveness for the transport vehicle management system was studies by comparing the transport plan, guidance and inspection status, safety training completion management, ect., and the reduction rate of chemical accidents. Results: The average number of chemical accidents in transport vehicles nationwide is 20 each year. And It is decreasing with the stabilization of the Chemical Substances Control Act('15.1.1). The first reason for the decrease in chemical accidents is the increase in submission of transport plans. Second, as the guidance and inspection rate increased every year, the shipper company's management of transport companies was naturally strengthened. Finally, it is judged that chemical accident caused by transport vehicles decrease through safety education. Conclusion: The current tranport vehicle management system of the Chemical Substances Control Act is effective. However, further research is needed to improve the practical and efficient transport vehicle management system.