• Journal of Sensor Science and Technology
• /
• v.12 no.5
• /
• pp.211-217
• /
• 2003

This paper is proposed that roadway recognition performance improvement for autonomous vehicle using magnetic markers that are embedded along the road center and the sensors mounted on a vehicle, and which changing of magnetic field that is measured along with vehicle driving. For Retrenchment of equipment cost, interval of markers is more expensive than existing method. In order to this, This paper is proposed that interval of markers is founded using magnetic field analysis, and which arrangement method of six magnetic sensors and control method of neural network. This paper is carried out magnetic field analysis, the acquiring of the training patterns, the training of the neural network and composition of steering control, and is verified that roadway recognition performance can improve using computer simulation with proposed methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.10
• /
• pp.1453-1461
• /
• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.6
• /
• pp.266-283
• /
• 2022

With the rapid development of autonomous vehicle technology, unexpected accidents are occurring. Therefore, it is necessary to minimize user accident damage through the development of autonomous traffic safety education. Since edge cases, accident type, and risk factor analysis are important for realistic education, overseas case studies and demonstrations were carried out, and based on this, two curriculum for service providers and general users were developed. The service provider curriculum consisted of OEDR, sudden stop, cut-in, take-over, defensive driving, system malfunction, policy and information security education, and the general user curriculum consisted of attention duty, take-over, operating design domain, accidents type, laws, functions, information security education.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.3
• /
• pp.427-432
• /
• 2023

In this study, MEC (: Multi-access Edge Computing) proposes a cloud service network configuration for various tests of autonomous vehicles to which V2X (: Vehicle to Everything) is applied in Wave, LTE, and 5G networks and MEC App (: Application) applied V2X service function test verification of two domains (operator (KT, SKT, LG U+), network type (Wave, LTE (including 3G), 5G)) in a specific region. In 4G networks of domestic operators (SKT, KT, LG U+ and Wave), MEC summarized the improvement effects through V2X function blocks and traffic offloading for the purpose of bringing independent network functions. And with a high level of QoS value in the V2X VNF of the 5G network, the traffic steering function scenario was demonstrated on the destination-specific traffic path.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.333-335
• /
• 2022

The changes caused by the 4th industrial revolution are accelerating the convergence of technologies due to the COVID-19 Pandemic. As a result, rapid changes are expected in various fields of society and industry. The shipping and logistics industry is also in urgent need of smartization due to changes in environment and technology. However, in the case of Korea, the smartization of shipping and logistics technology is insufficient compared to that of advanced countries. For smartization for complex business processing with many related entities, smooth communication and visual confirmation using the latest technologies are becoming important. Such visualization and communication will become more important in smart shipping and logistics This study intends to present an integrated communication tool between self-driving ships and container terminals for the realization of smart shipping and logistics. In particular, a study was conducted on the development of a digital twin-based communication tool that satisfies the requirements of ship berthing and loading/unloading operations in which various actors participate and process complex tasks.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.1
• /
• pp.31-48
• /
• 2024

The domestic automotive industry must consider a strategic shift from traditional automotive component manufacturing to align with future trends such as connectivity, autonomous driving, sharing, and electrification. This research conducted topic modeling on R&D projects in the future automotive sector funded by the Ministry of Trade, Industry, and Energy from 2013 to 2021. We found that topics such as sensors, communication, driver assistance technology, and battery and power technology remained consistently prominent throughout the entire period. Conversely, topics like high-strength lightweight chassis were observed only in the first period, while topics like AI, big data, and hydrogen fuel cells gained increasing importance in the second and third periods. Furthermore, this research analyzed the areas of concentrated investment for each period based on topic-specific government investment amounts and investment growth rates.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.1
• /
• pp.197-205
• /
• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• 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.

• Journal of Korean Society of Transportation
• /
• v.35 no.2
• /
• pp.116-128
• /
• 2017

Traffic accidents can be defined as a physical collision event of vehicles occurred instantaneously when drivers do not perceive the surrounding vehicles and roadway environments properly. Therefore, detecting the high potential events that cause traffic accidents with monitoring the interactions among the surroundings continuously by driver is the prerequisite for prevention the traffic accidents. For the analysis, basic data were collected to analyze interactions using a test vehicle which is equipped the GPS(Global Positioning System)-IMU(Inertial Measurement Unit), camera, radar and RiDAR. From the collected data, highway geometric information and the surrounding traffic situation were analyzed and then safety evaluation algorithm for driving vehicle was developed. In order to detect a dangerous event of interaction with surrounding vehicles, locations and speed data of surrounding vehicles acquired from the radar sensor were used. Using the collected data, the tangent and curve section were divided and the driving safety evaluation algorithm which is considered the highway geometric characteristic were developed. This study also proposed an algorithm that can assess the possibility of collision against surrounding vehicles considering the characteristics of geometric road structure. The methodology proposed in this study is expected to be utilized in the fields of autonomous vehicles in the future since this methodology can assess the driving safety using collectible data from vehicle's sensors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.224-226
• /
• 2016

With the technical development and rapid increase of private demand, the new market for unmanned vehicle combined with the characteristics of 'unmanned automation' and 'vehicle' is rapidly growing. Even though the pilot driving is currently allowed in some countries, there is no country that has institutionalized the formal driving of self-driving cars. In case of the existing vehicles, safety incidents are frequently happening due to the frequent malfunction of the rear sensor, blind spot of the rear camera, or drivers' carelessness. Once such minor flaws are complemented, the relevant regulations for the commercialization of self-driving car and small drone could be relieved. Contrary to the ultrasonic and laser sensors used for the existing vehicles, this paper aims to attempt the distance measurement by using the depth sensor. A depth camera calculates the distance data based on the TOF method calculating the time difference by lighting laser or infrared light onto an object or area and then receiving the beam coming back. As this camera can obtain the depth data in the pixel unit of CCD camera, it can be used for collecting depth data in real-time. This paper suggests to solve problems mentioned above by using depth data in real-time and also to design the obstacle avoidance system through distance measurement.