• 자동차안전학회지
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• 제4권2호
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• pp.32-36
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• 2012

The Automatic unmanned target object carrying system (AUTOCS) is developed for testing road vehicle radar and vision sensor. It is important for the target to reflect the realistic target characteristics when developing ASV or ADAS products. The AUTOCS is developed to move the pedestrian or motorcycle target for desired speed and position. The AUTOCS is designed that only payload target which is a manikin or a motorcycle is detected by the sensor not the AUTOCS itself. In order for the AUTOCS to have low exposure to radar, the AUTOCS is stealthy shaped to have low RCS(Radar Cross Section). For deceiving vision sensor, the AUTOCS has a specially designed pattern on outside skin which resembles the asphalt pattern. The AUTOCS has three driving modes which are remote control, path following and replay. The AUTOCS V.1 is tested to verify the radar detect characteristics, and the AUTOCS successfully demonstrated that it is not detected by a car radar. The result is presented in this paper.

• 한국측량학회지
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• 제39권5호
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• pp.253-263
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• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• 자동차안전학회지
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• 제9권4호
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• pp.32-37
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• 2017

For automated vehicles, the integrity and fault tolerance of environment perception sensor have been an important issue. This paper presents radar, vision, lidar(laser radar) fusion-based fault detection algorithm for autonomous vehicles. In this paper, characteristics of each sensor are shown. And the error of states of moving targets estimated by each sensor is analyzed to present the method to detect fault of environment sensors by characteristic of this error. Each estimation of moving targets isperformed by EKF/IMM method. To guarantee the reliability of fault detection algorithm of environment sensor, various driving data in several types of road is analyzed.

• 제어로봇시스템학회논문지
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• 제22권8호
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

• 자동차안전학회지
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• 제12권3호
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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.

• 전자공학회논문지
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• 제49권12호
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• pp.265-274
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• 2012

본 논문에서는 레이더와 비전센서 융합 기반의 움직임추정을 이용한 전방차량 검출 및 추적 방법을 제안한다. 제안하는 방법은 움직임추정을 통하여 레이더로 관측한 타겟의 부정확한 횡방향 위치를 보정할 뿐만 아니라 자차의 거동에 따른 자차-지면 간의 기하학적 관계 변화에 적응적으로 전방차량을 검출하고 추적한다. 또한 연산량 부담이 적은 특징점기반의 움직임추정 방법을 사용하여 차량을 검증하는 과정의 수행 횟수 감소를 도모하였다. 제안하는 움직임추정 방법으로 보정한 타겟이 기존의 방법에 비해 높은 시간적 일관성(temporal consistency)을 가지고 전방차량을 추적하는 것은 물론 다양한 도로환경에서 강건하게 전방차량을 검출하는 것을 실험을 통해 입증하였다.

• 대한임베디드공학회논문지
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• 제6권5호
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• pp.319-325
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• 2011

Recently, collision avoidance systems are under development to reduce the traffic accidents and driver comfort for automotive radar. Pulse radar can detect their range and velocities of moving vehicles using range gate and FFT(Fast Fourier Transform) of the doppler frequency. We designed the real time DSP(Digital Signal Processing) based automotive UWB(Ultra Wideband) radar, and implemented DSP to detect the range and velocity within 100ms for real time system of the automotive UWB radar. We also measured the range and velocity of a moving vehicle using designed automotive UWB radar in a real road environment.

• 한국지능시스템학회논문지
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• 제23권1호
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• pp.35-45
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• 2013

자동차의 자율주행기능 실현을 위해서는 기존의 지능형자동차 인식시스템 보다 강인하고 우수한 성능의 주행환경 인식시스템이 요구된다. 특히, 카메라와 레이저레이더 센서는 물체의 특징, 거리 등의 정보를 제공하는 대표적인 주행환경인식 센서로, 이를 이용한 단일센서기반 인식시스템 연구가 활발히 이루어지고 있다. 일반적으로 레이저레이더 센서의 거리정보는 도로의 구조, 차량, 보행자 등의 인식을 위하여 많이 사용되며, 카메라의 영상정보는 차선, 횡단보도, 표지판 등의 주행환경 인지에 사용된다. 하지만, 단일센서기반 인식시스템은 센서의 특성 및 주행환경에 의한 오검출 또는 미검출 발생률이 높기 때문에 자율주행기능 구현에 적합하지 않다. 따라서 단일센서기반의 인식시스템의 한계를 극복하기 위하여 카메라, 레이저레이더, GPS 등을 이용한 정보융합 인식시스템 개발이 필수적이다. 이 연구에서는 영상 및 레이저레이더의 정보융합을 통해 강인한 차선인식, 횡단보도 인식 등을 수행하는 자율주행자동차의 주행환경 인식기술을 개발하였다. 이 연구를 통해 개발된 주행환경 인식기술은 자율주행자동차에 적용되어 다양한 주행시험을 통해 신뢰성 및 안정성이 검증되었다.

• 자동차안전학회지
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• 제5권1호
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• pp.5-10
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• 2013

This paper describes a Forward Collision Warning (FCW) system based on the radar driven fusion with camera. The objective of FCW system is to provide an appropriate alert with satisfying the evaluation scenarios of US-NCAP and a driver acceptance. For this purpose, this paper proposed a data fusion algorithm and a collision warning algorithm. The data fusion algorithm generates information of fusion target depending on the confidence of camera sensor. The collision warning algorithm calculates indexes and determines an appropriate alert-timing by using analysis results of manual driving data. The FCW system with the proposed data fusion and collision warning algorithm was investigated via scenarios of US-NCAP and a real-road driving. It is shown that the proposed FCW system can improve the accuracy of an alarm-timing and reduce the false alarm in real roads.

• 한국지반환경공학회 논문집
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• 제23권8호
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• pp.17-22
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• 2022

공용 중에 연약지반의 침하로 발생된 교량 접속부의 단차 개선을 위해 도로관리기관에서는 지속적으로 덧씌우기를 실시하고 있다. 본 연구에서는 연약지반 내에 건설된 교량 9개소에 대해 접속부의 침하량을 추정하고자 1GHz 안테나를 차량에 탑재한 지표투과레이더 장비를 이용하였다. 포장 내부 조사가 가능한 지표투과레이더 기술로 아스팔트 도로에서 깊이 1m 수준까지 효과적으로 포장두께의 측정이 가능하였다. 노면 변형 조사결과와는 다르게, 9개 교량 접속부에서 측정된 포장두께 변화량은 최소 50mm에서 최대 600mm로 상당하였으며, 공동(空洞)의 발생 가능성도 높았다. 또한 각 교량 접속부의 부위별로는 증가된 포장두께의 차이가 50~250mm로 나타나 부등침하 위험이 존재하였다. 본 연구에서 지표투과레이더 결과에 근거하여 주행성 개선 및 침하부 유지관리 방안을 제시하였다.