• 한국ITS학회 논문지
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• 제22권6호
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• pp.264-283
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• 2023

자율주행차량 사고는 일반차량 사고와 다르게 기술적 문제, 환경, 운전자와의 상호작용 등 다양한 요인에 기인한 사고 발생 가능성이 존재한다. 향후 자율주행 기술의 진보로 기존의 사고원인 이외에도 새로운 이슈들이 대두될 것으로 예상되며, 이에 대응하기 위한 다양한 시나리오 기반의 접근법이 필요하다. 본 연구에서는 자율주행 사고 리포트인, CA DMV collision report와 자율주행모드 해제 보고서인 Disengagement report, 자율주행 실제 사고영상을 수집하여 자율주행차량 교통사고 시나리오를 개발하였다. 시나리오는 ISO 26262의 기능안전 시스템 failure mode에 기반하여 도출되었으며, 자율주행 기능의 다양한 이슈를 반영하고자 하였다. 본 연구를 통해 도출된 자율주행차량 시나리오는 향후 다양한 자율주행차량 교통사고 예방과 대비에 기여할 뿐만 아니라 자율주행 기술의 안전성을 향상시키는 데 중요한 역할을 할 것으로 기대한다.

• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.389-395
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• 2022

In order to ensure reliability the high-level automated driving such as Advanced Driver Assistance System (ADAS) and universal robot taxi provided by autonomous driving systems, the operation with high integrity must be generated within the defined Operation Design Domain (ODD). For this, the position and posture accuracy requirements of autonomous driving systems based on the safety driving requirements for autonomous vehicles and domestic road geometry standard are necessarily demanded. This paper presents localization requirements for safe road driving of autonomous ground vehicles based on the requirements of the positioning system installed on autonomous vehicle systems, the domestic road geometry standard and the dimensions of the vehicle to be designed. Based on this, 4 Protection Levels (PLs) such as longitudinal, lateral, vertical PLs, and attitude PL are calculated. The calculated results reveal that the PLs are more strict to urban roads than highways. The defined requirements can be used as a basis for guaranteeing the minimum reliability of the designed autonomous driving system on roads.

• 한국ITS학회 논문지
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• 제21권1호
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• pp.91-104
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• 2022

Level 3 자율주행차의 상용화가 가시화됨에 따라 자율주행차의 운행설계영역(ODD)이 고속도로 외 도심도로로 확대될 필요가 있다. 본 연구는 도심도로 내 인프라-자율차 간 협력주행 기반의 자율주행차 서비스에 대한 교통운영효율성 및 안전성 측면의 효과평가를 통해 도심도로 자율협력주행 서비스의 실효성을 분석하였다. 도심도로 자율협력주행 서비스의 구현 및 효과평가는 미시교통시뮬레이션모델을 활용하였으며, 각 서비스별 중점관리목표에 따른 개별적인 효과평가 지표를 선정하여 효과 분석에 활용하였다. 분석 결과, V2X 통신 기반의 자율협력주행 서비스를 통해 자율주행차량의 교통운영 효율성과 안전성이 향상됨을 확인하였고, 그 효과는 자율주행차의 시장점유율이 증가할수록 커지는 것으로 분석되었다. 본 연구는 단속류인 도심도로를 대상으로 V2X 통신 기반의 자율협력주행 서비스의 효과를 도출해낸 것에 의의가 있으며, 향후 자율협력주행 서비스 검증 기반이 마련되는데 기초자료로 활용될 수 있을 것으로 기대된다.

• 자동차안전학회지
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• 제15권2호
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• pp.21-27
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• 2023

This paper presents a simulation tool for developing and evaluating automated driving systems' lane change algorithm in urban congested traffic. The behavior of surrounding vehicles was modeled based on driver driving data measured in urban congested traffic. Surrounding vehicles are divided into aggressive vehicles and non-aggressive vehicles. The degree of aggressiveness is determined according to the lateral position to initiate interaction with the vehicle in the next lane. In addition, the desired velocity and desired time gap of each vehicle are all randomly assigned. The simulation was conducted by reflecting the cognitive limitations and control performance of the autonomous vehicle. It was possible to confirm the change in the lane change performance according to the variation of the lane change decision algorithm.

• 자동차안전학회지
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• 제13권4호
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• 자동차안전학회지
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• 제13권4호
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• 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.

• 한국ITS학회 논문지
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• 제20권5호
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• pp.172-185
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• 2021

자율주행시대가 우리 삶에 다가오면서 삶의 변화에 많은 영향을 미칠 것으로 예상된다. 자율주행자동차가 등장하면 운전자의 부담을 줄임으로 차내에서 생산적 가치가 확장되는 만큼 이를 평가할 수 있는 지표개발이 필요하다. 이번 연구는 경기도 직장인 중 승용차를 이용하는 통근자를 대상으로 자율주행 자동차가 통근시간 및 통근시간 만족도에 어떠한 영향을 미치는지 분석하였다. 통근시간 및 통근시간 만족도는 비선형 관계(V)가 도출되었다. 여기서, 자율주행시대에 영향받을 가능성이 높은 비선형 구간인 통근시간 70분 이상영역을 중심으로 이항로지스틱 모형을 통해 분석하였다. 분석결과 자율주행시대의 영향변수로는 건강도, 수면시간, 근무시간, 여가시간 등이 도출되었다. 자율주행자동차의 등장은 이러한 변수를 개선시킬 가능성이 높으므로 장거리 통근자의 통근시간 만족도는 개선될 가능성이 높다.

• 자동차안전학회지
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• 제14권2호
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• 자동차안전학회지
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• 제15권3호
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• pp.34-42
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• 2023

Simulation-based virtual testing is known to be a major requirement for verifying the safety of autonomous driving functions. However, in the simulation environment, there is a difficulty in that all driving environments related to the autonomous driving system must be realistically modeled. In particular, C-ITS (Cooperative-Intelligent Transport Systems) is essential for urban autonomous driving of Lv.4, but the approach to modeling for C-ITS service in the MILS (Model in the Loop Simulation) environment is not yet clear. Therefore, this paper aims to deal with V2X (Vehicle to Everything) modeling methods to effectively apply C-ITS-based autonomous cooperative driving services in the MILS environment. First, major C-ITS services and use cases for autonomous cooperative driving are analyzed, and a modeling method of V2X signals for C-ITS service simulation is proposed. Based on the modeled V2X messages, the validity of the proposed approach is reviewed through simulations on the C-ITS service use case.