Earticle

최근 4차 산업혁명이 화두로 등장하면서 자율주행자동차의 중요성과 관심이 높아지고 있다. 전 세계적으로 시험 운행이 늘 어나면서 자율주행자동차와 관련된 사고도 발생하고 있으며, 이에 대한 사이버 해킹 위협 가능성도 높아지고 있다. 미국, 영국, 독일을 포함한 여러 국가들은 이러한 추세를 반영하여 자율주행자동차의 사이버 해킹에 대응하기 위한 가이드라인을 만들거 나 기존의 법률을 개정하고 있다. 국내의 경우 자율주행자동차의 제한적인 임시운행이 이루어지고 있으나, 자율주행자동차 해 킹으로 인한 사고 발생 시 적용할 법제가 미흡한 상태이다. 본고에서는 기존의 관련 법률 체계를 분석하고 이를 바탕으로 자 율주행자동차 사이버 해킹으로 인한 민사, 형사, 행정 책임 문제를 살펴보면서, 자율주행자동차 특성에 맞는 사고 책임 관련 법률체계를 제안하고 각 법제의 구성요소에 대해서 분석하여 이슈사항을 도출하며, 추가적으로 간략한 개선방안도 제시한다.

As the 4th industrial revolution has recently become a hot topic, the importance of autonomous vehicles has increased and interest has been increasing worldwide, and accidents involving autonomous vehicles have also occurred. With the development of autonomous vehicles, the possibility of a cyber-hacking threat to the car network is increasing. Various countries, including the US, UK and Germany, have developed guidelines to counter cyber-hacking of autonomous vehicles, In the case of Korea, limited temporary operation of autonomous vehicles is being carried out, but the legal system to be applied in case of accidents caused by vehicle network hacking is insufficient. In this paper, based on the existing legal system, we examine the civil liability caused by the cyber hacking of the autonomous driving car, while we propose a law amendment suited to the characteristics of autonomous driving car and a legal system improvement plan that can give sustainable trust to autonomous driving car.

This paper proposes a design method for a driving simulator and driver assistance system in a vehicle network environment. Each module is on the same Controller Area Network(CAN) bus line for vehicle network configuration. To compose hardware in the loop simulation (HILS), a testbed similar to the actual vehicle environment is configured, and a virtual city and vehicle objects are created using a driving simulator. In particular, the vehicle network was configured by modularity and CAN ID applied from the modules according to the priority order. In the experiment, the possibility of using the vehicle control module was confirmed through the vehicle driving scenario in terms of the driver’s various emotions and situations. By acquisition of bio signal data from attached electrodes on the driver’s hand, the response time for changing the vehicle control mode and the control response time was measured in the manual driving mode, and it was confirmed that the control latency time is less than the commercialized response time of 830ms in autonomous vehicle control.

The advanced information and communication technology gives vehicles another role of the third digital space, merging a physical space with a virtual space in a ubiquitous society. In the ubiquitous environment, the vehicle becomes a sensor node, which has a computing and communication capability in the digital space of wired and wireless network. An intelligent vehicle information system with a remote control and diagnosis is one of the future vehicle systems that we can expect in the ubiquitous environment. However, for the intelligent vehicle system, many issues such as vehicle mobility, in-vehicle communication, service platform and network convergence should be resolved. In this paper, an in-vehicle gateway is presented for an intelligent vehicle information system to make an access to heterogeneous networks. It gives an access to the server systems on the internet via CDMA-based hierarchical module architecture. Some experiments was made to find out how long it takes to communicate between a vehicle's intelligent information system and an external server in the various environment. The results show that the average response time amounts to 776ms at fixec place, 707ms at rural area and 910ms at urban area.

In modern economy, any firm cannot sustain by itself. Whatever the type of a firm, it is tangled up with other firms in any relation of supply and demand, partnership or competition, etc. In 2021, for example, chip shortage caused by the spread of COVID-19 and the sanction on Chinese chipmakers stroke the global automotive industry. As a result, major car original equipment manufacturers (OEMs) such as GM, Ford, Toyota, and many others stopped their production, which serially stopped their automotive component supplier’s production as well. However, the business magazine Forbes warns the worse situation with the messages “Battery scarcity could dwarf chip shortage impact on global auto sales” and “The semiconductor shortage will cut a total of 8.1 million cars from global production between 2021 and 2023, while between 2022 and 2029, 18.7 million rechargeable electric cars will be lost because of battery cell shortages” (Winton 2021). Due to the interdependence between firms, if any disruption erupts in the industry, its ripple effect impacts the cross-over in a multi-tiered value chain (Cachon and Lariviere, 2001). In 2010s, electric vehicle industry emerged by strong environment regulations and the hyper growth of the industry pioneers who are strongly based on information technology (IT), such as Tesla. Actually, automotive industry including electric vehicle is no longer limited to manufacturing, but is growing rapidly through a complex value chain combined with advanced IT technologies. Therefore it is highly required to understand the complex pattern and sustainability of the highly entangled system. In this research we investigate how the electric vehicle industry emerges and the resilience of the value chain changes. To tackle the research question, we introduce the novel methodological framework based on complex network theory and agent based model approach. From the empirical data on electric vehicle value chain of 6 years, we apply the framework to address the evolution of the industry using weighted network analysis (Figure 1). We found that the electric vehicle industry becomes more heterogeneous structure (Figure 2). This means that the companies. In complex network theory, the heterogeneous network structure has been well known to have the properties of “double-edge swords” in the robustness of the system, that is, it is usually very robust, but the collapse of a few central firms can cause whole system to collapse with a huge ripple effect (Albert et al. 2000). With the understandings of structural properties of electric vehicle value chain network, we examine the resilience of the network with introducing the agent based simulation model. From our simulation results, as year goes, the electric vehicle value chain becomes less resilient. Furthermore, from the result of the distinction between individual firm’s impact of unweighted and weighted aspect, we found some firm’s influence would be underestimated when one only consider the unweighted network properties. Our novel approach based on weight network analysis and agent based modeling would more capture the characteristics of electric vehicle industry and shed light on understanding systemic risks due to the individual firm’s disruption.

The advance of mobile and telematics technologies has produced vehicles with various convenient services for drivers. Specifically lots of researches and several technologies have been developed to provide services of a remote vehicle diagnosis and control. The existing and representative product for a vehicle control is a RCS (remote control system), but it has a problem of short control distance and fragile security. In this paper, a telematics terminal embedded with CDMA and GPS is designed, which can be connected to the Internet. It allows a driver with a cellular phone to remotely diagnosis and control a vehicle via wireless network and SMS.

최근 차량 내부와 외부 네트워크를 연결하는 차량 내 네트워크 기술이 발전함에 따라 CAN(Controller Area Network)을 통해 다양한 정보를 실시간으로 교환할 수 있다. 하지만 CAN 네트워크는 사이버 공격에 취약 하다. 따라서 본 연구에서는 지도 및 비지도 딥러닝 모델 기반 침입 탐지 방법을 제안한다. 실험 데이터로는 CAN ID로 구성된 시계열 데이터를 사용하였다. 지도학습 기반 침입 탐지 방법은 전반적으로 높은 탐지 성능을 보였다. 정상 데이터로만 학습하여 탐지하는 비지도 기반 방식의 경우에도 CAN에 오작동을 일으킬 수 있는 새로운 공격 유형(Spoofing, DoS)에 대한 탐지율이 높았다. 비지도학습을 위해 LSTM(Long Short-Term Memory) 기반 오 토인코더와 IF(Isolation Forest)를 사용하였다. 따라서 제안한 기법은 알려진 공격 외에 새로운 공격을 탐지하는 데도 효과적인 것을 확인하였다.

Recently, with the development of in-vehicle network technology that connects the internal and external networks of vehicle, various information can be exchanged in real time through CAN (Controller Area Network). However, the CAN networks are vulnerable to cyberattacks. Therefore, in this study, we propose an intrusion detection method based on supervised and unsupervised deep learning models. Time series data consisting of CAN IDs were used as experimental data. The supervised learning-based intrusion detection method showed high overall detection performance. Even in the case of an unsupervised method that learns and detects only normal data, the detection rate for new attack types (Spoofing, DoS) that can cause malfunctions in CAN was high. For unsupervised learning, an LSTM (Long Short-Term Memory) based Autoencoder and IF (Isolation Forest) were used. Therefore, it was confirmed that the proposed technique is effective in detecting new attacks in addition to known attacks.

현재 IT기술과 자동차기술을 융합한 지능형 자동차에 대한 연구가 활발히 진행중에 있으며 많은 새로운 서비스 모델들이 개발중에 있다. 지능형자동차에 대한 개발이 활발하게 이루어지면서 무선 네트워크를 이용한 다양 한 서비스가 제공이 되고 있다. 이러한 지능형 자동차가 무선 네트워크를 이용한 서비스가 제동되면서 다양한 보안 위협 요소가 도출되고 있다. 본 논문에서는 지능형 자동차의 외부에서 네트워크에 침입하여 지능형자동차의 보안위 협 요소를 분석하고 지능형 자동차의 보안 솔루션의 인증 모델에 대한 기법을 제공하고자 한다. 인증 모델의 경우 신체의 고유성을 식별할 수 있는 생체인식을 이용한 인증기법을 제안한다.

Studies on the intelligent vehicles that are fused with IT and intelligent vehicle technologies are currently under active discussion. And many new service models for them are being developed. As intelligent vehicles are being actively developed, a variety of wireless services are support. As such intelligent vehicles use wireless network, they are exposed to the diverse sources of security risk. This paper aims to examine the factors to threaten intelligent vehicle, which are usually intruded through network system and propose the security solution using biometric authentication technique. The proposed security system employs biometric authentication technique model that can distinguish the physical characteristics of user.

Recently, due to the expansion of data communication between objects, research related to data communication technology applied to vehicles is being actively conducted. This study selects a network with Wi-Fi 6, which is advantageous in bandwidth, communication speed, and wireless saturation of a wireless network for mobile terminal data communication, and designs and implements Wi-Fi 6 in a vehicle network. In addition, a continuous variable communication structure is proposed to enable high speed data switching in consideration of the characteristics of mobile communication terminal devics, indicating that connection operation and response speed are improved compared to Wi-Fi standard communication methods, and it can be extended to a system for road networks and autonomous driving by expanding it to various event data communication between vehicles.

이 연구의 목적은 Bluetooth를 활용한 자동차의 내부망 통신네트워크를 해킹공격으로부터 예방하기 위한 대응방안을 제시하기 위함이다. 이를 위해 2장에서는 자동차 통신네트워크의 정의와 내부망 통신네트워크의 종류에 대해서 살펴보 았다. 3장에서는 자동차 내부망 통신네트워크 해킹위험성을 분석하기 위해 Bluetooth에 의한 해킹범죄사례를 살펴보았 다. 4장에서는 본 연구의 결과로서 첫째, 『자동차안전기준에 관한 규칙』개정이 이루어져야 한다. 동법에서는 전자제 어시스템의 정의 및 기준사항과 안전운행을 위한 제작 및 정비를 규정해 놓았음에도 불구하고 전자제어시스템을 대상 으로 한 해킹범죄 예방 및 방어와 관련된 보안프로그램 혹은 펌웨어 등의 제작과 관련된 규정이 없는 실정이다. 둘째, 자동차의 전자제어시스템 기술의 복잡성에 기인된 자동차 통신네트워크를 보호하고자 자동차 통신네트워크 보호 법률 이 신설되어야 한다.

The purpose of this study is to analyse motor vehicle communication network hacking attacks and to provide its prevention. First, the definition of motor vehicle communication network was provided and types of in-vehicle communication network were discussed. Also, bluetooth hacking attack cases were analysed in order to illustrate dangers of hacking attacks. Based on the analysis, two preventive measures were provided. First, Motor Vehicle Safety Standard Law should be revised. Although the law provides the definition of electronic control system and its standards as well as manufacturing and maintenance for safe driving standards, the law does not have standards for electronic control system hacking prevention and defensive security programs or firmware. Second, to protect motor vehicle communication network, it is necessary to create new laws for motor vehicle communication network protection.

비디오를 이용한 비전기반 감시에서 움직이는 객체의 추적은 GMM (Gaussian Mixture Model)을 사용한 배경영상과 현재영상의 차이법을 이용한다. 문턱치를 통해 생성된 이진영상을 이용하여 객체 추적을 할 경우 객체 정보가 아닌 그림자에 의하여 객체가 병합되는 현상이 나타난다. 본 논문에서는 신경망(Backpropagation Neural Network)을 이용하여 그림자를 제거하는 방법을 제안하였다. 10개의 동영상에서 객체영역과 캐스트그림자(Cast-Shadow)영역의 훈련용 이미지에서 특징 값을 추출하여 신경망을 훈련시켰다. 캐스트그림자를 제거하는 방법은 이진영상의 객체로 추정되는 영역에서 그림자를 분리하는 방법을 기초로 하며 기존의 그림자 제거 알고리즘 (SNP, SP, DNM1, DNM2, CNCC)보다 그림자 제거 성능이 (16.2%, 38.2%, 28.1%, 22.3%, 44.4%)로 높게 나타났다.

The moving object tracking in vision based observation using video uses difference method between GMM(Gaussian Mixture Model) based background and present image. In the case of racking object using binary image made by threshold, the object is merged not by object information but by Cast-Shadow. This paper proposed the method that eliminates Cast-Shadow using backpropagation Neural Network. The neural network is trained by abstracting feature value form training image of object range in 10-movies and Cast-Shadow range. The method eliminating Cast-Shadow is based on the method distinguishing shadow from binary image, its Performance is better(16.2%, 38.2%, 28.1%, 22.3%, 44.4%) than existing Cast-Shadow elimination algorithm(SNP, SP, DNM1, DNM2, CNCC).