• Title/Summary/Keyword: Driving algorithm

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Development of I2V Communication-based Collision Risk Decision Algorithm for Autonomous Shuttle Bus (자율주행 셔틀버스의 통신 정보 융합 기반 충돌 위험 판단 알고리즘 개발)

  • Lee, Seungmin;Lee, Changhyung;Park, Manbok
    • Journal of Auto-vehicle Safety Association
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    • v.11 no.3
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    • pp.19-29
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    • 2019
  • Recently, autonomous vehicles have been studied actively. Autonomous vehicles can detect objects around them using their on board sensors, estimate collision probability and maneuver to avoid colliding with objects. Many algorithms are suggested to prevent collision avoidance. However there are limitations of complex and diverse environments because algorithm uses only the information of attached environmental sensors and mainly depends on TTC (time-to-Collision) parameter. In this paper, autonomous driving algorithm using I2V communication-based cooperative sensing information is developed to cope with complex and diverse environments through sensor fusion of objects information from infrastructure camera and object information from equipped sensors. The cooperative sensing based autonomous driving algorithm is implemented in autonomous shuttle bus and the proposed algorithm proved to be able to improve the autonomous navigation technology effectively.

Development of a Washout Algorithm for a Vehicle Driving Simulator Using New Tilt Coordination and Return Mode (새로운 경사 변환과 복귀 성분을 고려한 차량 운전 시뮬레이터 워시아웃 알고리즘 개발)

  • 강유진;유기성;이민철
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.7
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    • pp.634-642
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    • 2004
  • Unlike actual vehicles, a vehicle driving simulator is limited in kinematic workspace and bounded on dynamic characteristics. So it is difficult to simulate dynamic motions of a multi-body vehicle model. In order to overcome these problems, a washout algorithm which controls the workspace of the simulator within the kinematic limitation is needed. However, a classical washout algorithm contains several problems such as generation of wrong sensation of motions by filters in tilt coordination, requirement of trial and error method in selecting the proper cut-off frequencies, difficulty in returning the simulator to its origin using only high pass filters and etc. This paper proposes a new tilt coordination method as an algorithm which gives more accurate sensations to drivers. In order to reduce time for returning the simulator to its origin, a new washout algorithm that the proposed algorithm selectively onset mode from high pass filters and return mode from error functions is proposed. As a result of this study, the results of the proposed algorithm are compared with the results of classical washout algorithm through the human perception models. Also, the performance of the suggested algorithm is evaluated by using human perception and sensibility of some drivers through experiments.

The Performance Improvement for an Active Noise Contort of Automotive Intake System under Rapidly Accelerated Condition (급가속시 자동차 흡기계의 능동소음제어 성능향상)

  • 이충휘;오재응;이유엽;이정윤
    • Transactions of the Korean Society of Automotive Engineers
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    • v.11 no.6
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    • pp.183-189
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    • 2003
  • The study of the automotive noise reduction has been concentrated on the reduction of the automotive engine noise because the engine noise is the major cause of automotive noise. However, many studies of automotive engine noise led to the interest of the noise reduction of the exhaust and intake system. Recently, the active control method is used to reduce the noise of an automotive exhaust and intake system. It is mostly used the LMS(Least-Mean-Square) algorithm as an algorithm of active control because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, Filtered-X LMS (FXLMS) algorithm is applied to an Active Noise Control system. However, the convergence performance of LMS algorithm went bad when the FXLMS algorithm was applied to an active control of the induction noise under rapidly accelerated driving conditions. So, in order to solve this problem, the modified FXLMS algorithm is proposed. In this study, the improvement of the control performance using the modified FXLMS algorithm under rapidly and suddenly accelerated driving conditions was identified. Also, the performance of an active control using the LMS algorithm under rapidly accelerated driving conditions was evaluated through the theoretical derivation using a chirp signal to have similar characteristics with the induction noise signal.

Driving and Position Sensing Algorithm for an Electrostatic Actuator Using Pulse-width Modulation (펄스폭 변조를 이용한 정전형 액추에이터의 구동 및 위치 검출 알고리즘)

  • Min, Dong-Ki;Jeon, Jong-Up
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.17 no.3
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    • pp.65-70
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    • 2008
  • Capacitive position sensing with modulation technique is widely used in electrostatic actuator applications. To maximize the electrostatic force and the position-sensing gain, capacitors for driving and capacitors for sensing are shared, i.e, after applying the driving voltage with high-frequency modulating signals using op amps, the position is demodulated from the modulated signal. In high-voltage applications, however, low bandwidth of a high-voltage op amp hinders adding the high-frequency modulating signal to the driving voltage. In this paper, new and very simple driving and sensing method is proposed, in which the pulse-width modulated driving voltage eliminates the need of the high-frequency modulating signal for position sensing. This new algorithm is proved by the simulation results using Matlab/SIMULINK.

Development of a Longitudinal Control Algorithm based on V2V Communication for Ensuring Takeover Time of Autonomous Vehicle (자율주행 자동차의 제어권 전환 시간 확보를 위한 차간 통신 기반 종방향 제어 알고리즘 개발)

  • Lee, Hyewon;Song, Taejun;Yoon, Youngmin;Oh, Kwangseok;Yi, Kyongsu
    • Journal of Auto-vehicle Safety Association
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    • v.12 no.1
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    • pp.15-25
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    • 2020
  • This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.

Development of an Intelligent Autonomous Control Algorithm and Test Vehicle Performance Verification (지능형 자율주행 제어 알고리즘 개발 및 시험차량 성능평가)

  • Kim, Won-Gun;Yi, Kyong-Su
    • Proceedings of the KSME Conference
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    • 2007.05a
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    • pp.861-866
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    • 2007
  • This paper presents development of a vehicle lateral and longitudinal control for autonomous driving control and test results obtained using an electric vehicle. Sliding control theory has been used to develop a vehicle speed and distance control algorithm. The longitudinal control algorithm that maintains safety and comfort of the vehicle consists of a cruise and STOP&GO control depending on traffic conditions. Desired steering angle is determined through the lateral position error and the yaw angle error based on preview optimal control. Motor control inputs have been directly derived from the sliding control law. The performance of the autonomous driving control which is integrated with a lateral and longitudinal control is investigated by computer simulations and driving test using an electric vehicle. Electric vehicle system consists of DC driving motor, an electric power steering system, main controller (Autobox)

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Driver's Eye Blinking Detection Method based on Template Matching using Line Profile (라인 프로파일을 이용한 템플릿 매칭 기반의 운전자 눈 깜박임 검출 방법)

  • Kim, Young Jae;Shin, Seung Seob;Kim, Kwang Gi
    • Journal of Korea Multimedia Society
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    • v.20 no.6
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    • pp.873-881
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    • 2017
  • Prevention of drowsy driving is one of the important issues for safe driving. In this study, the algorithm for detection of drowsy driving has been developed. The algorithm was implemented by applying template matching and line profile, which detects eye blink. The accuracy of eye detection and blink detection was $97.45{\pm}3.67%$ and $98.50{\pm}0.92%$, which was resulted from the verification experiment that 21 subjects participated. Consequently, the algorithm is expected to be used to prevent sleep-deprived driving.

Development of a Washout Algorithm for a Vehicle Driving Simulator Using New Tilt Coordination and Return Mode

  • You Ki Sung;Lee Min Cheol;Kang Eugene;Yoo Wan Suk
    • Journal of Mechanical Science and Technology
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    • v.19 no.spc1
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    • pp.272-282
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    • 2005
  • A vehicle driving simulator is a virtual reality device which makes a man feel as if he drove an actual vehicle. Unlike actual vehicles, the simulator has limited kinematical workspace and bounded dynamic characteristics. So it is difficult to simulate dynamic motions of a multi-body vehicle model. In order to overcome these problems, a washout algorithm which controls the workspace of the simulator within the kinematical limitation is needed. However, a classical washout algorithm contains several problems such as generation of wrong sensation of motions by filters in tilt coordination, requirement of trial and error method in selecting the proper cut-off frequencies and difficulty in returning the simulator to its origin using only high pass filters. This paper proposes a washout algorithm with new tilt coordination method which gives more accurate sensations to drivers. To reduce the time in returning the simulator to its origin, an algorithm that applies selectively onset mode from high pass filters and return mode from error functions is proposed. As a result of this study, the results of the proposed algorithm are compared with the results of classical washout algorithm through the human perception models. Also, the performance of the suggested algorithm is evaluated by using human perception and sensibility of some drivers through experiments.

An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm

  • Son, SuRak;Jeong, YiNa;Lee, ByungKwan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.12
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    • pp.5842-5861
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    • 2019
  • A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

A Method of Detecting the Aggressive Driving of Elderly Driver (노인 운전자의 공격적인 운전 상태 검출 기법)

  • Koh, Dong-Woo;Kang, Hang-Bong
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.11
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    • pp.537-542
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    • 2017
  • Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.