• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• 자동차안전학회지
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• 제15권2호
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• pp.42-48
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• 2023

In recent years, automated vehicles have garnered attention in the multidisciplinary research field, promising increased safety on the road and new opportunities for passengers. High-Definition (HD) maps have been in development for many years as they offer roadmaps with inch-perfect accuracy and high environmental fidelity, containing precise information about pedestrian crossings, traffic lights/signs, barriers, and more. Demonstrating autonomous driving requires verification of driving on actual roads, but this can be challenging, time-consuming, and costly. To overcome these obstacles, creating HD maps of real roads in a simulation and conducting virtual driving has become an alternative solution. However, existing HD maps using high-precision data are expensive and time-consuming to build, which limits their verification in various environments and on different roads. Thus, it is challenging to demonstrate autonomous driving on anything other than extremely limited roads and environments. In this paper, we propose a new and simple method for implementing HD maps that are more accessible for autonomous driving demonstrations. Our HD map combines the CARLA simulator and OpenStreetMap (OSM) data, which are both open-source, allowing for the creation of HD maps containing high-accuracy road information globally with minimal dependence. Our results show that our easily accessible HD map has an accuracy of 98.28% for longitudinal length on straight roads and 98.42% on curved roads. Moreover, the accuracy for the lateral direction for the road width represented 100% compared to the manual method reflected with the exact road data. The proposed method can contribute to the advancement of autonomous driving and enable its demonstration in diverse environments and on various roads.

• 드라이브 ㆍ 컨트롤
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• 제17권4호
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• pp.80-86
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• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.

• 대한음성학회지:말소리
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• 제50호
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• pp.99-110
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• 2004

The normal transmission characteristics of sound are hardly obtained due to the various noises and structural factors in a running car environment. It is due to the channel distortion of the original source sound recorded by microphones, and it seriously degrades the performance of the speech recognition in real driving environments. In this paper we analyze the degree of intelligibility under the various sound distortion environments by channels according to driving speed with respect to speech transmission index(STI) and compare the STI with rates of speech recognition. We examine the correlation between measures of intelligibility depending on sound pick-up patterns and performance in speech recognition. Thereby we consider the optimal location of a microphone in single channel environment. In experimentation we find that high correlation is obtained between STI and rates of speech recognition.

• ETRI Journal
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• 제45권5호
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• 자동차안전학회지
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• 제14권3호
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• 로봇학회논문지
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• 제18권3호
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• pp.271-280
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• 2023

FMCW (Frequency Modulated Continuous Wave) radar system is widely used in autonomous driving and navigation applications due to its high detection capabilities independent of weather conditions and environments. However, radar signals can be easily contaminated by various noises such as speckle noise, receiver saturation, and multipath reflection, which can worsen sensing performance. To handle this problem, we propose a learning-free noise removal technique for radar to enhance detection performance. The proposed method leverages adaptive thresholding to remove speckle noise and receiver saturation, and wavelet transform to detect multipath reflection. After noise removal, the radar image is reconstructed with the geometric structure of the surrounding environments. We verify that our method effectively eliminated noise and can be applied to autonomous driving by improving the accuracy of odometry and place recognition.

• 자동차안전학회지
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• 제14권4호
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• pp.60-69
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• 2022

Recently, the importance of simulation validation in a virtual environment for autonomous driving system validation is increasing. At the same time, interest in the advancement of the virtual driving environment is also increasing. To develop autonomous driving technology, a simulation environment similar to the real-world environment is needed. For this reason, not only the road model is configured in the virtual driving environment, but also the driving environment configuration that includes the surrounding environments -traffic, object, etc- is necessary. In this article, FMTC, which is a test bed for autonomous vehicles, is implemented in a virtual environment and advanced to form a virtual driving environment similar to that of real FMTC. In addition, the similarity of the virtual driving environment is verified through comparative analysis with the real FMTC.

• 대한교통학회지
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• 제35권4호
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• pp.332-347
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• 2017

자율주행기술이 교통류에 미치는 영향을 분석하기 위해서는 자율차와 비자율차 간의 상호작용을 분석하는 것이 중요한 이슈이다. 특히 자율주행기술을 활용한 유용한 서비스 중의 하나인 군집주행은 주변의 비자율 차량의 주행행태에 영향을 미칠 수 있다. 본 연구의 목적은 군집주행 환경에서 비자율차의 차로변경행태 분석하는 것이며, 3단계의 실험 및 조사를 수행하였다. 1단계 영상기반 인지특성 분석을 통해 군집주행 환경에서 어떠한 반응행태를 보일 것인지를 조사하였으며, 2단계 주행시뮬레이션 실험을 통해 비자율차의 차로변경행태를 분석하였다. 차로변경행태를 분석하기 위해 차로변경시간과 교통류의 안전성을 나타낼 수 있는 지표인 가속소음을 이용하였으며, 자율차의 시스템 보급률(Market Penetration Rate, MPR)과 피실험자 인적요소에 따른 비자율차의 주행행태 차이를 비교 분석하였다. 마지막 단계인 NASA-TLX(NASA Task Load Index)를 통해 비자율차 운전자의 작업부하를 평가하였다. 분석결과 군집차량군 주변의 비자율차 운전자는 심리적인 부담감을 느끼며, MPR이 증가할수록 차로변경시간이 길어지고 30-40대 운전자 또는 여성 운전자의 경우 안전성이 낮아지는 것으로 나타났다. 본 연구에서 도출된 결과는 자율차와 비자율차의 상호작용을 반영한 보다 현실성 높은 교통시뮬레이션 실험 시 기초자료로 활용될 수 있고, 이를 기반으로 자율협력주행 환경에서 적용 가능한 교통운영관리전략 수립을 효과적으로 지원할 것으로 기대된다.

• 디지털산업정보학회논문지
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• 제8권1호
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• pp.1-7
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• 2012

In this paper Vehicle driving simulator have been used in the development and modification of models. A real-time simulation system and washout algorithm for an excavator have been developed for a driving simulator with six degrees of freedom. An interesting question, "how the 6 DOF Driving Simulator can be controlled optimally for the various tasks?" is not easy to be answered. This paper presents the hardware and software developed for a driving simulator of construction vehicle. A simulator can reduce cost and time a variety of driving simulations in the laboratory. Using its 6 DOF Simulator can move in various modes, and perform dexterous tasks. Driving simulators have begun to proliferate in the automotive industry and the associated research community. This effort involves the real-time dynamic of wheel-type excavator the design and manufacturing of the Stewart platform an integrated control system of the platform and three-dimensional graphic modeling of the driving environments.