• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.5
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• pp.308-321
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• 2024

The government aims to commercialize Level 4 autonomous buses and shuttles by 2025, expanding the demonstration of Level 3 autonomous buses on urban streets, where various factors affecting the driving conditions raise safety concerns. This study used actual driving data from autonomous buses in Pangyo to identify the disengagement locations and analyzed the static and dynamic road environment factors. The results showed that the disengagement of autonomous buses frequently occurs near intersections and bus stops, with those at the bus stops due primarily to operational procedures requiring driver intervention. Excluding these, the analysis identified crosswalks and driveways as static factors, whereas fog and rain are dynamic factors affecting disengagements. Based on these findings, recommendations were proposed to minimize disengagements, aiming to improve the operational safety of autonomous buses in Korea and address upcoming challenges.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.5
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• pp.293-307
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• 2024

In mixed traffic conditions, the conservative driving behavior of autonomous vehicles (AV) would negatively affect overall traffic performance. In order to manage mobility and safety in mixed traffic conditions, it is essential to scientifically evaluate driving behavior using autonomous driving data collected from real-world. This study proposed a methodology to evaluate the driving behavior of autonomous vehicles (AV) and manual vehicles (MV) at different types of intersections using the Waymo Open Dataset. Urban street were identified through video data, and the autonomous driving conservativeness index (ADCI) was devised to compare the difference in time-to-collision (TTC) based conflict rates between AV and MV in car following situations. The results showed that unsignalized 4-way intersections had the highest ADCI value, indicating greater conservativeness in driving behavior. This indicates the necessity of analyzing the driving behavior of each road section and deriving support measures to prevent AV from negatively affecting the overall traffic performance in mixed traffic conditions. The methodology of this study is expected to serve as foundational for analyzing factors affecting AV using real-world datasets.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.94-109
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• 2024

As autonomous vehicles are allowed to drive on public roads, there is an increasing amount of on-road data available for research. It has therefore become possible to analyze impacts of autonomous vehicles on traffic safety using real-world data. It is necessary to use indicators that are well-representative of the driving behavior of autonomous vehicles to understand the implications of them on traffic safety. This study aims to derive indicators that effectively reflect the driving stability of autonomous vehicles by analyzing the driving behavior using the Waymo Open Dataset. Principal component analysis was adopted to derive indicators with high explanatory capability for the dataset. Driving stability indicators were separated into longitudinal and lateral ones. The road segments on the dataset were divided into four based on the characteristics of each, which were signalized and unsignalized intersections, tangent road section, and curved road section. The longitudinal driving stability was 35.48% higher in the curved road sections compared to the unsignalized intersections. With regard to the lateral driving stability, the driving stability was 76.08% higher in the signalized intersections than in the unsignalized intersections. The comparison between curved and tangent road segments showed that tangent roads are 146.87% higher regarding lateral driving stability. The results of this study are valuable for the further research to analyze the impact of autonomous vehicles on traffic safety using real-world data.

• Annual Conference of KIPS
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• 2020.05a
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• pp.497-499
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• 2020

자율주행 차량이 많아지고 차량의 ECU가 고도화되면서 정확한 차량의 데이터를 획득하고 분석하여 활용하는 것이 중요해지고 있다. 현재에는 내연 기관 차량의 ECU 데이터를 얻기 위해서 OBDII 포트(규격)에 기반한 CAN동선을 주로 이용하고 있다. 하지만 OBDII 규격을 통해서 연비와 같은 중요한 차량 정보를 얻는 경우, 변환식 (MAF 센서(흡입 공기량 센서)와 공기/연료 비율을 이용)의 오차 범위가 커서 데이터의 정확도가 낮다. 본 연구에서는 머신 러닝 기법 중에 하나인 회귀 기법을 통해서 기존의 계산보디 더 정확한 연비를 구할 수 있는 모델을 개발하였다. 이러한 모델 개발을 통하여 차량의 RAW 데이터를 기반으로 필요한 차량 데이터를 정확하게 구할 수 있게 되었으며 20회가 넘는 실 도로주행을 통해서 본 모델의 정확도를 검증하였다.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.285-292
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• 2017

This paper describes a recursive least-squares based convergent algorithm for driving characteristic classification for personalized autonomous driving. Recently, various researches on autonomous driving technology have been conducted for level 4 fully autonomous driving. In order for commercialization of the autonomous vehicle, personalized autonomous driving is required to minimize passenger's insecureness to the autonomous vehicle. To address this problem. this study proposes mathematical model that represents driving characteristics and recursive least-squares based algorithm that can estimate the defined characteristics. The actual data of two drivers has been used to derive driving characteristics and the hypothesis testing method has been used to classify two drivers. It is shown that the proposed algorithms can derive driving characteristics and classify two drivers reasonably.

• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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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.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.249-255
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• 2018

This paper presents a human-centered control algorithm for personalized autonomous driving based on the integration of inverse time-to-collision and time headway. In order to minimize the sense of difference between driver and autonomous driving, the human-centered control technology is required. Driving characteristics in case that vehicle drives with the preceding vehicle have been analyzed and reflected to the longitudinal control algorithm. The driving characteristics such as acceleration, inverse time-to-collision, time headway have been analyzed for longitudinal control. The control algorithm proposed in this study has been constructed on Matlab/Simulink environment and the performance evaluation has been conducted by using actual driving data.

• Journal of Auto-vehicle Safety Association
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• v.10 no.4
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• pp.40-49
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• 2018

Currently, a lot of researches about high risk test scenarios for autonomous vehicle and advanced driver assistance systems have been carried out to evaluate driving safety. This study proposes new type of test scenario that evaluate the driving safety for autonomous vehicle by reconstructing accident database of national automotive sampling system crashworthiness data system (NASS-CDS). NASS-CDS has a lot of detailed accident data in real fields, but there is no data of accurate velocity in accident moments. So in order to propose scenario generation method from accident database, we try to reconstruct accident moment from accident sketch diagram. At the same step, we propose an accident of occurrence frequency which is based on accident codes and road shapes. The reconstruction paths from accident database are integrated into evaluation of simulation environment. Our proposed methods and processor are applied to MILS (Model In the Loop Simulation) and VILS (Vehicle In the Loop Simulation) test environments. In this paper, a reasonable method of accident reconstruction typology for autonomous vehicle evaluation of feasibility is proposed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.160-173
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• 2021

Recently, research on the development of autonomous vehicles has increased worldwide. Moreover, a means to identify and analyze the characteristics of traffic accidents of autonomous vehicles is needed. Accordingly, traffic accident data of autonomous vehicles are being collected in California, USA. This research examined the characteristics of traffic accidents of autonomous vehicles. Primarily, traffic accident data for autonomous vehicles were analyzed, and the text data used text-embedding techniques to derive major keywords and four topics. The methodology of this study is expected to be used in the analysis of traffic accidents in autonomous vehicles.