• Journal of IKEEE
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• v.20 no.4
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• pp.351-360
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• 2016

Recently, machine learning algorithms, especially deep learning-based algorithms, have been receiving attention due to its high classification performance. Among the algorithms, Convolutional Neural Network(CNN) is known to be efficient for image processing tasks used for Advanced Driver Assistance Systems(ADAS). However, it is difficult to achieve real-time processing for CNN in vehicle embedded software environment due to the repeated operations contained in each layer of CNN. In this paper, we propose a hardware accelerator which enhances the execution time of CNN by parallelizing the repeated operations such as convolution. Xilinx ZC706 evaluation board is used to verify the performance of the proposed accelerator. For $36{\times}36$ input images, the hardware execution time of CNN is 2.812ms in 100MHz clock frequency and shows that our hardware can be executed in real-time.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.2
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• pp.219-226
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• 2017

This paper addresses the effective and quantitative image DB construction for the development of front looking camera systems. The automotive industry has expanded the capability of front camera solutions that will help ADAS(Advanced Driver Assistance System) applications targeting Euro NCAP function requirements. These safety functions include AEB(Autonomous Emergency Braking), TSR(Traffic Signal Recognition), LDW(Lane Departure Warning) and FCW(Forward Collision Warning). In order to guarantee real road safety performance, the driving image DB logged under various real road conditions should be used to train core object classifiers and verify the function performance of the camera system. However, the driving image DB would entail an invalid and time consuming task without proper guidelines. The standard working procedures and design factors required for each step to build an effective image DB for reliable automotive front looking camera systems are proposed.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.23-29
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• 2022

Among the various functions of ADAS (Advanced Driver Assistance System), the most important and representative function to the safety of vehicle passengers is AEB (Autonomous Emergency Braking system). In South Korea, laws are in progress from 2022 for making it mandatory for passenger vehicles to be installed. And as AEB-equipped vehicles continues to increase in the future, the demand for accident analysis related to the AEB function is expected to increase in the future. In order to find out the operating limits of AEB, it is necessary to consider the situations exceeding the standards covered by EuroNCAP. Therefore we have performed four experiments in this study, including situations encountered in real-word traffic conditions, i.e., an oblique stop of Global Vehicle Target (GVT) and ADAS sensor failures. These experimental results are expected to be of great help in accurate and reliable accident analysis by considering them when analyzing traffic accidents for ADAS vehicles.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.35-41
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• 2024

In order to verify autonomous driving scenarios and safety, a lot of driving and accident data is needed, so various organizations are conducting classification and analysis of traffic accident types. In this study, it was determined that accident recording devices such as EDR (Event Data Recorder) and DSSAD (Data Storage System for Automated Driving) would become an objective standard for analyzing the causes of autonomous vehicle accidents, and traffic accidents that occurred from 2015 to 2020 were analyzed. Using the database system of IGLAD (Initiative for the Global Harmonization of Accident Data), approximately 360 accident data of EDR-equipped vehicles were classified and their characteristics were analyzed by comparing them with accident types of ADAS (Advanced Driver Assistance System)-equipped vehicles. It will be used to develop autonomous vehicle accident investigation guidelines in the future.

• Information and Communications Magazine
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• v.32 no.8
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• pp.38-44
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• 2015

본고에서는 자동차의 자율주행이라는 최종의 목표를 위해 필요한 여러 가지 필수 기술 중 하나라고 할 수 있는 자동차의 자기 위치 인식을 위한 측위 기술에 대해 소개하고 그와 관련된 여러 연구 개발 동향을 살펴보고자 한다. 또한, 지능형 운전보조 시스템(ADAS, Advanced Driver Assistance System)에서 필수적인 고정밀 전자 지도(High Precision Map)가 자동차의 자기 위치 인식 정확도 향상에 어떤 방법으로 활용되는 지에 대해서도 알아 보고자 한다.

• Journal of Auto-vehicle Safety Association
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• v.12 no.3
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• pp.34-42
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• 2020

ADAS (Advanced Driver Assistance Systems) uses sensors such as camera, radar, lidar and GPS (Global Positioning System). Among these sensors, the camera has many advantages compared with other sensors. The reason is that it is cheap, easy to use and can identify objects. In this paper, therefore, a theoretical formula was proposed to obtain the distance from the vehicle's front wheel to the lane using a monocular camera. And the validity of the theoretical formula was verified through the actual vehicle test. The results of the actual vehicle test in scenario 4 resulted in a maximum error of 0.21 m. The reason is that it is difficult to detect the lane in the curved road, and it is judged that errors occurred due to the occurrence of significant yaw rates. The maximum error occurred in curve road condition, but the error decreased after lane return. Therefore, the proposed theoretical formula makes it possible to assess the safety of the LKA system.

• Journal of IKEEE
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• v.22 no.4
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• pp.970-977
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• 2018

The autonomous vehicle is based on an advanced driver assistance system (ADAS) consisting of a sensor that collects information about the surrounding environment and a control module that determines the measured data. As interest in autonomous navigation technology grows recently, an easy development framework for ADAS beginners and learners is needed. However, existing development and verification methods are based on high performance vehicle simulator, which has drawbacks such as complexity of verification method and high cost. Also, most of the schemes do not provide the sensing data required by the ADAS directly from the simulator, which limits verification reliability. In this paper, we present an interactive ADAS development and verification framework using a 3D vehicle simulator that overcomes the problems of existing methods. ADAS with image recognition based artificial intelligence was implemented as a virtual sensor in a 3D car simulator, and autonomous driving verification was performed in real scenarios.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.96-108
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• 2017

The current collision warning and avoidance system(CWAS) is one of the representative Advanced Driver Assistance Systems (ADAS) that significantly contributes to improve the safety performance of a vehicle and mitigate the severity of an accident. However, current CWAS mainly have focused on preventing a forward collision in an uninterrupted flow, and the prevention performance near intersections and other various types of accident scenarios are not extensively studied. In this paper, the safety performance of Vision-Sensor (VS) and Radar-Sensor(RS) - based collision warning systems are evaluated near an intersection area with the data from Naturalistic Driving Study(NDS) of Second Strategic Highway Research Program(SHRP2). Based on the VS and RS data, we newly derived sixteen vehicle-to-vehicle accident scenarios near an intersection. Then, we evaluated the detection performance of VS and RS within the derived scenarios. The results showed that VS and RS can prevent an accident in limited situations due to their restrained field-of-view. With an accident prevention rate of 0.7, VS and RS can prevent an accident in five and four scenarios, respectively. For an efficient accident prevention, a different system that can detect vehicles'movement with longer range than VS and RS is required as well as an algorithm that can predict the future movement of other vehicles. In order to further improve the safety performance of CWAS near intersection areas, a communication-based collision warning system such as integration algorithm of data from infrastructure and in-vehicle sensor shall be developed.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.62-69
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• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.255-263
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• 2016

Lane Keeping Assistance System (LKAS) is a kind of Advanced Driver Assistance Systems (ADAS) which are developed to automate/ adapt/ enhance vehicle systems for safety and better driving. The main system function of LKAS is to support the driver in keeping the vehicle within the current lane. LKAS acquires information on the position of the vehicle within the lane and, when required, sends commands to actuators to influence the lateral movement of the vehicle. Recently, the vehicles equipped with LKAS are commercially available in a few vehicle-advanced countries and the installation of LKAS increases for safety enhancement. The test procedures for LKAS evaluations are being discussed and developed in the international committees such as ISO (the International Organization for Standardization) and UNECE (United Nations Economic Commission for Europe). In Korea, the evaluations of LKAS for vehicle safety are planned to be introduced in 2016 KNCAP (Korean New Car Assessment Program). Therefore, the test procedures of LKAS suitable for domestic road and traffic conditions, which accommodate international standards, should be developed. In this paper, some bullet points of the test procedures for LKAS are discussed and proposed by extensive researches of previous documents and reports, which are released in public in regard to lateral test procedures including LKAS and Lane Departure Warning System (LDWS). And then, to evaluate the validity of the proposed test procedures, a series of experiments were conducted using commercially available two vehicles equipped with LKAS. Later, it can be helpful to make a draft considering domestic traffic situations for test procedures of LKAS.