• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1695-1696
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• 2008

KRRI (Korea Railroad Research Institute) is developing a bimodal tram since the 2003s. The vehicle will be used in the public transportation system. The bimodal tram has the advantages of both bus and train. Bus system has the advantages of flexibility of the routes delivering passengers to the destination and easy accessibility. Train is to meet the scheduled arrival and massive public transportations. The vehicle is the rubber tired tram and is all wheel steered single articulation. The propulsion system is configured by CNG hybrid system. The length of the vehicle is 18m. The vehicle lanes will be marked with permanent magnets that are buried in the road. The vehicle can be automatically operated by navigation control system (NCS). In this paper, we introduce the driving system of the bimodal tram which is developed by KRRI.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.206-211
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• 2011

In this paper, a control algorithm for the improvement of yaw and velocity stability of electrical vehicle with two or four in-wheel motors is proposed. The vehicle is modeled with independently operative in-wheel motor wheels. Different frictions on the wheels are regarded as disturbances, which causes driving instability. In this situation the proposed algorithm enables stabilizing the yaw motion and velocity of vehicle simultaneously. The proposed PID controller is composed with two techniques, which enhance the disturbance reject and point tracking performances. One is nonlinear gain function and the other one is improved integral controller operating as time based weight function. Simulation is conducted to reveal its efficient performance.

• 한국자동차공학회논문집
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• 제3권6호
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• pp.154-161
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• 1995

There are several kinds of driving conditions according to the characteristic of each vehicle diver. Automaker produces vehicle strong enough to satisfy this several driving conditions at the point of vehicle durability. In order to develop the car in a short period, Automaker engineer tests vehicle at serveral accelerated durability test roads. Before testing the vehicle durability, test engineer must know how much this test road severe than general field road which is composed of high way, city road, paved road and unpaved road. This paper suggests two types of road severity test method that is using relative fatigue life prediction method and using absolute fatigue life prediction method, and also present the merits and demerits of two test methods.

• 대한기계학회논문집A
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• 제29권1호
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• 자동차안전학회지
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• 제13권4호
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• pp.106-113
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• 2021

In this study, The behavior of an autonomous vehicle in an intersection accident situation is predicted. Based on a representative intersection accident situation from actual intersection accident database, simulation was performed by applying the automatic emergency braking algorithm used in the autonomous driving system. Accident reconstruction was performed based on the accident report of the representative accident situation. After applying the autonomous driving system to the accident-related vehicle, the tendency of intersection accidents that may occur in autonomous vehicles was identified and analyzed.

• 자동차안전학회지
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• 제15권3호
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• pp.17-26
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• 2023

This paper presents extended state feedback control based on impulse response for lane change of autonomous driving vehicle. The triple characteristic root of path tracking system and longitudinal velocity determine feedback gains. We suggest a resemblance of impulse response curve of the system and lane change trajectory of the vehicle. The root affects the duration of lane change and lateral acceleration. The effect of limited lateral acceleration and saturation of steering angle will be analyzed and discussed. Finally, simulation results will show the trajectory of lane change based on impulse response under limitation of lateral acceleration.

• 반도체디스플레이기술학회지
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• 제22권3호
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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.

• 자동차안전학회지
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• 제15권4호
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• pp.71-78
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• 2023

This study presents the direction of data triggers and elements to be recorded in automated vehicles in the future in relation to the event data recorder (EDR) and data storage system for automated driving (DSSAD). It does not distinguish between the EDR and DSSAD, but suggests data triggers and elements in preparation for overall automated vehicle accidents and dangerous situations. To propose, the current status of discussions on EDR/DSSAD internationally and the case of investigating accidents with automated vehicles under temporary driving licenses in Korea were analyzed. Based on the analysis, the direction of data triggers and elements of the EDR/DSSAD of automated vehicles were presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4518-4540
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• 2019

With the development of IT convergence technology, autonomous driving has gradually developed; however, the vehicle is still operated by the driver, who should always be in good health - but sometimes, this is not the case. It is especially dangerous to drive when drowsy, and unable to fully concentrate on driving, such as when taking certain medicines, or through fatigue. Drowsy driving is at least eight times more dangerous than normal driving, and as dangerous as drunk driving. Previous research has looked at technology to detect drowsiness, in order to wake up drivers when necessary, or to safely stop the vehicle. Furthermore, many studies have been conducted to find out when drowsiness occurs. However, it is more desirable for the driver to take sufficient rest during a break, in order to be able to continue to focus and drive. In other words, it is important to maintain a normal state before drowsiness. In this study, we introduce a sound source to increase driver concentration and prevent drowsiness, another that can improve the quality of sleep, and a system that produces these sound sources. The proposed system has a noise reduction effect of about 15 dB. We have confirmed that the proposed sound induces an EEG of the desired form.