• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.84-90
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• 2022

With the popularization of autonomous driving technology, safety has emerged as a more important criterion. However, there are no assessment protocol or methods for AES (Autonomous Emergency Steering). So, this study proposes AES assessment protocol and scenario corresponding to collision avoidance Car-to-Car scenario of Euro NCAP in order to prepare for obstacles that appear after the emergency steering of LV (Leading Vehicle) avoiding obstacles in front of. Autoware-based autonomous driving stack is developed to test and simulate scenario in CARLA. Using developed stack, it is confirmed that obstacle avoidance is successfully performed in CARLA, and the AES performance of VUT (Vehicle Under Test) is evaluated by applying the proposed assessment protocol and scenario.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.03a
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• pp.209-216
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• 1999

Prefabricated piles used for construction of highway bridges are most of steel pipe piles and few of prestressed concrete piles. Its installation and inspection are less controllable and have much uncertainty due to changes in subsoil and groundwater conditions. However, most of these piles have been controlled using outdated pile driving formula such as Hiley's formula which models just the energy conservation due to its simple applicability in the field. This formula results in overstriking or sometimes understocking due to buckling of pile head. Engineers cannot ensure by the formula whether pile is installed properly. To compensate the drawbacks of excising pile formula, parameters in Hiley's formula and 55 formula are reviewed. Final sets used in pile formula and PDA test results(E.O.I.D) are measured during pile driving along the depth. These measured results along the depth were compared with each other and with N values, so that relations between the each result could be inferred. Also the factor of safety which can be used for pile driving formula are suggested.

• Journal of Auto-vehicle Safety Association
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• v.3 no.2
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• pp.28-33
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• 2011

This paper presents driving path estimation algorithm for adaptive cruise control system and advanced emergency braking system using multi-sensor fusion. Through data collection, yaw rate filtering based road curvature and vision sensor road curvature characteristics are analyzed. Yaw rate filtering based road curvature and vision sensor road curvature are fused into the one curvature by weighting factor which are considering characteristics of each curvature data. The proposed driving path estimation algorithm has been investigated via simulation performed on a vehicle package Carsim and Matlab/Simulink. It has been shown via simulation that the proposed driving path estimation algorithm improves primary target detection rate.

• Journal of Auto-vehicle Safety Association
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• v.8 no.4
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• pp.31-37
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• 2016

Since real road experiments have many restrictions, a multi-vehicle traffic simulator can be an effective tool to develop and evaluate fully automated driving systems. This paper presents multi-vehicle environment simulation tool to develop and evaluate motorway automated driving systems. The proposed simulation tool consists of following two main parts: surrounding vehicle model and environment sensor model. The surrounding vehicle model is designed to quickly generate rational complex traffic situations of motorway. The environment sensor model depicts uncertainty of environment sensor. As a result, various traffic situations with uncertainty of environment sensor can be proposed by the multi-vehicle environment simulation tool. An application to automated driving system has been conducted. A lane changing algorithm is evaluated by performance indexes from the multi-vehicle environment simulation tool.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• 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.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.

• Journal of the Korean Society of Safety
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• v.23 no.3
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• pp.71-78
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• 2008

This study aims to identity the age group where driving performance significantly decreases based on the data collected from the Korea Transportation Safety Authority's driver aptitude tests in 2006. The test includes following six driving simulator-based tests: estimation of moving objects' speed, estimation of stopping distance, three tests for drivers' multi-task ability, and kinetic depth perception. These six test results were utilized for the identification of the age threshold applying the CART technique, suggesting driving ability significantly be decreased over 50s. This finding was confirmed by two analyses using the accident history data containing the information of accident and non-accident drivers and the degree of accident severity. The results of this study imply that accident prevention efforts should be enhanced over a wider range of age group than the current practice where the age of 65 is generally applied for the threshold dividing senior and non-senior driver groups.

• Journal of the Korean Society of Safety
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• v.22 no.1 s.79
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• pp.61-66
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• 2007

Car crashes are the leading cause of death for persons of every age. Specially, human-related factor has been known to be the primary causal factor of such crashes than vehicle-and environmental-related factors. There are various studies to analyze driver's behavior and characteristics in driving for reducing the car crashes in many areas of car engineering, psychology, human factor, etc. However, there are almost no studies which analyze mainly the human errors in driving and estimate their probabilities in terms of human reliability analysis. This study estimates the probability of human error in driving, i.e. driver error probability. First, fifty driver errors are investigated through DBQ (Driver Behavior Questionnaire) revision and the error likelihoods in driving are collected which are judged by skillful drivers using revised DBQ. Next, these likelihoods are converted into driver error probabilities using the results that verbal probabilistic expressions are changed into quantitative probabilities. Using these probabilities we can improve the warning effects on drivers by indicating their driving error likelihoods quantitatively. We can also expect the reduction effects of car accident through controlling especially dangerous error groups which have higher probabilities. Like these, the results of this study can be used as the primary materials of safety education on drivers.