• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.129-143
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• 2019

As the importance of autonomous vehicle safety is emphasized, the application of ISO-26262, a development verification guideline for improving safety and reliability, and the safety verification of autonomous vehicles are becoming increasingly important, in particular, SAE standard level 3 or higher level autonomous vehicles detect and decision the surrounding environment instead of the human driver. Therefore, if there is and failure or malfunction in the autonomous driving function, safety may be seriously affected. So autonomous vehicles, it is essential to apply and verity the safety concept against failure and malfunctions. In this study, we study the fault injection scenarios for safety evaluation and verification of autonomous vehicles using ISO-26262 part3 process and STPA were studied and safety measures for safety concept design were studied through simulation bases fault injection test.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.172-191
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• 2023

This study was undertaken to strategize the mixed traffic operation of autonomous vehicles in the pilot zone. This was achieved by analyzing the changes expected when autonomous vehicles are mixed in the autonomous vehicle pilot zone. Although finding a safe and efficient traffic operation strategy is required for the pilot zone to serve as a test bed for autonomous vehicles, there is no available operation strategy based on the mixture of autonomous vehicles. In order to presents a traffic operation strategies for each period of autonomous vehicle introduction, traffic efficiency and safety analysis was performed according to the autonomous vehicle market percentage rate. Based on the analysis results, the introduction stage was divided into introductory stage, transition period, and stable period based on the autonomous vehicle market share of 30% and 70%. This study presents the following traffic operation strategies. Considering the traffic flow operation strategy, we suggest the advancement of the existing road infrastructure at the introductory stage, and operating an autonomous driving lane and the mileage system during the transition period. We also propose expanding the operation of autonomous driving lanes and easing the speed limit during the stable period. In the traffic safety strategy, we present a manual and legal system for responding to autonomous vehicle accidents in the introductory stage, an analysis of the causes of autonomous vehicle accidents and the implementation of preventive policies in the transition period, and the advancement of the autonomous system and the reinforcement of the security system during the stable period. Through the traffic operation strategy presented in this study, we foresee the possibility of preemptively responding to the changes of traffic flow and traffic safety expected due to the mixture of autonomous vehicles in the autonomous vehicle pilot zone in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.595-609
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• 2020

In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.973-982
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• 2018

Purpose: The purpose of this study is to develop an effective simulation modeling formalism for autonomous control systems, such as unmanned aerial vehicles and unmanned surface vehicles. The proposed simulation modeling formalism can be used to evaluate the quality and effectiveness of autonomous control systems. Methods: The proposed simulation modeling formalism is developed by extending the classic DEVS (Discrete Event Systems Specifications) formalism. The main advantages of the classic DEVS formalism includes its rigorous formal definition as well as its support for the specification of discrete event models in a hierarchical and modular manner. Results: Although the classic DEVS formalism has been a popular modeling tool, it has limitations in describing an autonomous control system which needs to make decisions by its own. As a result, we proposed an extended DEVS formalism which enables the effective description of internal decisions according to its conditional variables. Conclusion: The extended DEVS formalism overcomes the limitations of the classic DEVS formalism, and it can be used for the effectiveness simulation of autonomous weapon systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.15-29
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• 2021

The signal intersection is the most challenging space for autonomous vehicles. To promote the safe driving of autonomous vehicles on urban roads with traffic signals, autonomous vehicles need to receive traffic signal information from infrastructure through V2I communication. Thus, a protocol for providing traffic signal information was added to the standard traffic signal controller specification of the National Police Agency. On the other hand, there are technical limitations when applying this to digital traffic signal controllers because the protocols are defined mainly for analog traffic signal controllers. Therefore, this study proposes developing a signal information linkage module to provide traffic signal information from a digital traffic signal controller to an autonomous vehicle and an algorithm improvement method that can provide accurate traffic signal information at the time of traffic signal transition.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.21-26
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• 2022

Commercial use of autonomous vehicles is to come soon. So far most of responsibility of the accident is on the human driver with conventional vehicles whereas that will be on the car OEM and transportation related organizations with autonomous vehicles, which asks car OEM's and government to do vast study of car crash in various conditions. Test protocols need amendment and to be newly enacted to reflect new findings from the study aforementioned. Rigid stationary barrier and moving or stationary deformable barrier as well as car to car test which is same as actual accident can be utilized to simulate the crash happening on the road. Among those 3 test methods, rigid stationary barrier is most economic and has good repeatability. Limitation as well as advantage of the rigid stationary barrier is studied through comparison between car to car crash and oblique rigid barrier crash.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.477-483
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• 2022

In this paper, we propose waste collection vehicle system with a safety device for worker safety and an autonomous driving function. The steering system is applied as MDPS (Motor Drive Power Steering) system to control the waste collection vehicle of the internal combustion engine. Safety-related errors is prevented through redundancy brake of the integrated system and the control braking system. In order to ensure safety between workers and waste collection vehicles, work guidelines and safety devices for emergency stop in case of danger are applied to vehicles. In addition, this research is conducted on improving the working efficiency through vehicle condition monitoring system and a short-range control system for field test. This research is aimed to secure stability through demonstration and contribute to the industrialization of unmanned waste collection vehicles.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.391-399
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• 2018

V2X communication is a technology in which a vehicle exchanges information with various entities such as other vehicles, infrastructure, networks, pedestrians, etc. through a wired or wireless network. Recently, V2X communication technology has been steadily developed and recently it has played an important role in autonomous cooperation driving technology combined with autonomous vehicle technology. Autonomous vehicles can utilize the external information received via V2X communication to extend the recognition range of existing sensors and to support more safe and natural autonomous driving. In order to operate these autonomous cooperative vehicles on public roads, the security and reliability of autonomous V2X communication should be verified in advance. In this paper, we present test scenarios and test procedures of secure V2X communication for cooperative automated driving and present verification results.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.1-15
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• 2022

In order to trust and use autonomous vehicles, safe driving on the road must be guaranteed. For this, the first infrastructure to be equipped with autonomous driving is traffic safety facility. On the other hand, autonomous vehicles(Level 3) and general vehicles are driving on the road, it is necessary to additionally manage existing general traffic safety facilities. In this study, a field management system for traffic safety facilities based on autonomous driving infrastructure was studied, and a pilot field management system was implemented in the demonstration area(Pangyo). The pilot system consists of a GNSS(Global Navigation Satellite System) receiver, a field management equipment, and a field management app. As a result of field demonstration,, it was confirmed that traffic safety facility information was easily transmitted and received even in downtown areas and that could be efficiently operated and managed. It is expected that the results of this study will be used as reference materials for the spread of autonomous driving infrastructure to local governments and infrastructure construction in the future.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.