• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• Annual Conference of KIPS
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• 2022.11a
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• pp.845-847
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• 2022

교통사고의 원인 중 90%는 졸음운전과 같은 운전자의 부주의 때문에 발생하고 있다. 정부에서도 사고로 인한 인명피해 심각성을 인지하고 2019년부터 전방충돌방지 시스템과 차선이탈 경고 장치 등 ADAS(Advanced Driver Assistance Systems)를 의무적으로 적용하도록 규제를 강화하는 추세이다. 충돌사고를 예방하기 위해 본 논문에서는 영상처리를 기반으로 하여 객체 검출, 차간거리 측정, 후미등 검출, 차선 검출 기능을 적용하여 위험한 상황을 감지하고 운전자에게 경고 알림을 제공하는 System을 개발한다. 더 나아가 다양한 모빌리티 서비스에 이를 활용할 수 있는 방안을 제공한다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.940-942
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• 2023

교통사고 통계에 따르면 비보호 구역 내 도로에서 발생하는 교통사고 발생률이 일반 도로보다 30% 높은 수준임이 밝혀졌다. 기존 첨단 운전자 지원 시스템(ADAS: Advanced Driver Assistance Systems)은 다양한 사고 시나리오가 존재하는 비보호 구역에 적용하기에는 한계가 있다. 본 논문은 이러한 문제에 대응하기 위해 기존 ADAS 기능을 확장하여 예측과 판단이 어려운 비보호 구역에서 AI 분석을 통해 운전자에게 주행 가능 여부를 시각적으로 제공하는 시스템을 개발하고자 한다. 이 시스템은 운전자에게 경고와 지원을 제공함으로써 비보호 구역 내 교통사고를 예방할 수 있다.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.1018-1023
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• 2010

The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.6-10
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• 2019

This paper describes effectiveness of accident reduction on vehicles equipped with AEB using accident data occurring in Korea. During the statistical period, we used the number of vehicles which are covered by auto insurance and the number of accidents. To maximize the reduction effect of accidents caused by the driver's carelessness, the analysis was limited to Physical Damage Coverage that covers the cost of repairing or replacing the damaged vehicle caused by the driver's fault. Due to Personal Information Protection Law, it was not capable of comparing the same vehicle using Vehicle Identification Number in this study. Instead of that, we used it as a similar vehicle, so there are limits to the comparison and analysis results. As a result of this study, we have found that the effect of reducing accidents was different depending on the vehicle class, but it was generally concluded that the number of accidents decreased when the vehicle was equipped with an AEB system. Domestic research on the AEB effect of reducing accidents is not active yet. Therefore, it is absolutely essential to analyze the effects according to various conditions such as driver's age, occupation and gender as well as expanding the study models in the future.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.51-58
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• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.169-178
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• 2018

Currently, various sensors have been used for advanced driver assistance systems. In order to overcome the limitations of individual sensors, sensor fusion has recently attracted the attention in the field of intelligence vehicles. Thus, vision and radar based sensor fusion has become a popular concept. The typical method of sensor fusion involves vision sensor that recognizes targets based on ROIs (Regions Of Interest) generated by radar sensors. Especially, because AVM (Around View Monitor) cameras due to their wide-angle lenses have limitations of detection performance over near distance and around the edges of the angle of view, for high performance of sensor fusion using AVM cameras and radar sensors the exact ROI extraction of the radar sensor is very important. In order to resolve this problem, we proposed a sensor fusion scheme based on commercial radar modules of the vendor Delphi. First, we configured multiple radar data logging systems together with AVM cameras. We also designed radar post-processing algorithms to extract the exact ROIs. Finally, using the developed hardware and software platforms, we verified the post-data processing algorithm under indoor and outdoor environments.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.297-305
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• 2017

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop 'completely autonomous driving'. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the 'completely autonomous driving' automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.979-989
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• 2016

Traversable region detection is an essential step for advanced driver assistance systems and self-driving car systems, and it has been conducted by detecting lanes from input images. The performance can be unreliable, however, when the light condition is poor or there exist no lanes on the roads. To solve this problem, this paper proposes an algorithm which utilizes the information about the number of lanes and texture analysis. The proposed algorithm first specifies road region candidates by utilizing the number of lanes information. Among road region candidates, the road region is determined as the region in which texture is homogeneous and texture discontinuities occur around its boundaries. Traversable region is finally detected by dividing the estimated road region with the number of lanes information. This paper combines the proposed algorithm with a lane detection-based method to construct a system, and simulation results show that the system detects traversable region even on the road with poor light conditions or no lanes.