• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.251-257
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• 2022

Self-driving vehicles are a type of smart vehicle with the help of ICT technology, which means a vehicle that operates without the intervention of a driver.Vehicles with vehicle safety communication technology (V2X) applied use information detected from various sensors or other vehicles/infrastructures to enable the smart vehicle to accurately and quickly predict the driver's potential danger situation, contributing to more stable autonomous driving. In this paper, among V2X communication technologies, a vehicle-to-vehicle communication (V2V) simulation communication technology is used to present a scenario for preventing collisions in autonomous vehicles. A vehicle collision prevention system based on V2V simulated communication was implemented and the suggested collision prevention application scenario was demonstrated. The suggested collision prevention utilization scenario can be considered as one application case of V2V communication technologies that are currently being developed/applied.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.735-745
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• 2023

The purpose of this study is to examine how the control of an agent according to a driver's social status affects user experience factors in a multi-user environment of self-driving vehicles. We conducted a user study where participants viewed four scenarios (route changing/parking x accepting/declining a fellow passenger's command) and answered a survey, followed by a post-hoc interview. Results showed that either the routing scenario or accepting a passenger's command scenario had higher usefulness (convenience, effectiveness, efficiency) than their counterparts. Regardless of the car owner's social status, participants rated AI agents more positively when they met their goals effectively. They also stressed that vehicle owners should always be in control of their agents. This study can provide guidelines for designing future autonomous driving scenarios where an agent interacts with a driver, and passengers.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.327-333
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• 2016

An autonomous vehicle is a convergence of artificial intelligence and a vehicle which can drive itself while analyzing the real-time situation on a road without a driver. A lot of research achievements have been revealed through the media and Google is considered to be the best leading company in this field. The use of patent information which contains various information such as bibliographic data and information about technologies is a good way to find out the R&D direction of a company and develop a reasonable strategy. This study is aimed at investigating the direction to which Google focuses its R&D capabilities and establishing strategies for technology development. Google's patents about autonomous vehicles were collected and the degree of research bias was analyzed using Social Network Analysis based on citations indicating the quality of a patent. Based on the results, the strategies for technology development was eventually proposed. As a result, it was revealed that Google focused its R&D capabilities on the part of hardware control to make up for its lack of hardware-oriented technologies. As of now, Google obtained remarkable achievements, so it seems reasonable that last-movers consider cooperative research with Google.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.55-69
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• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.

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

• Journal of Service Research and Studies
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• v.11 no.3
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• pp.43-54
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• 2021

This study focused on indoor spaces and convenience devices among vehicle interior designs suitable for the autonomous driving era, and presented an interior design model for future automobiles by applying the STEEP analysis method. The service design methodology is applied to deal with changes in display devices installed for the purpose of rearranging layouts and providing driver-centered information. Changes in types and installation locations of displays for various purposes such as connected and infotainment are expected. In particular, through this analysis, trends and experiences through indoor interior research in future self-driving cars will be studied, and subsequent studies will be used as basic data for actual development and application. Key drivers were extracted after deriving future trends linking the research project conducted in five stages to STEEP and consulting experts through FGI. Through this, it was later presented as a direction for indoor design. Through user-centered participatory design methods, emotional keyword derivation methods were used, summarized the derived drivers in five major trends in the future society, and each derived drivers were grouped to consider the relevant technology fields, and added elements to the autonomous driving level. This is an indoor ray viewed from the perspective of various social issues as well as personal tendencies in the future self-driving car industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.173-187
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• 2018

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.2 s.6
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• pp.213-220
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• 1995

Map Matching is the method for correcting the current position from dead reckoning in Car Navigation System. In this paper, we proposed the new map matching algorithm that can correct the positioning error caused by sensors and digital map data around the cross road area. To do this, first we set the error boundary of the cross road area by combining the relative error of moving distance and the absolute error of road length, second, we find out the starting point of turning within the determined error boundary of the cross point area, third, we compare the turning angle of the car to the angle of each possible road, and the last, we decide the matched road. We used wheel sensor as a speed sensor and used optical fiber gyro as a directional sensor, and assembled the sensors to the notebook computer. We testified our algorithm by driving the Daejeon area-which is a part of south Korea-as a test area. And we proved the efficiency by doing that.