• Convergence Security Journal
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• v.22 no.2
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• pp.77-90
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• 2022

Domestic and foreign automakers compete to lead the autonomous vehicle industry through continuously developing self-driving technologies. These self-driving technologies are evolving with dependencies on the connection between vehicles and other objects such as the environment of cars and roads. Therefore, cyber security vulnerabilities become more likely to occur in the self-driving environment, so it is necessary to prepare for them carefully. In this paper, we model the threats in autonomous vehicles and make the checklist to securely countermeasure them.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.25-34
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• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.16-24
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• 2012

This paper proposes a method utilizing Differential Global Position System (DGPS) with Real-Time Kinematic (RTK) and pre-built Geo-graphic Information System (GIS) to detect lane departure of a vehicle. The position of a vehicle measured by DGPS with RTK has 18 cm-level accuracy. The preconditioned GIS data giving accurate position information of the traffic lanes is used to set up coordinate system and to enable fast calculation of the relative position of the vehicle within the traffic lanes. This relative position can be used for safe driving by preventing the vehicle from departing lane carelessly. The proposed system can be a key component in functions such as vehicle guidance, driver alert and assistance, and the smart highway that eventually enables autonomous driving supporting system. Experimental results show the ability of the system to meet the accuracy and robustness to detect lane departure of a vehicle at high speed.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.130-139
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• 2011

In this paper, vehicle tracking algorithm using hue transformation in HIS color model is proposed. the proposed algorithm is installed on the road of the two horizontal virtual data sampling lines. The difference images are detected between the frame and the frame, respectively and also detected in the vehicle by using the hue color distribution to determine identity and lane changes. To examine the effectiveness of proposed algorithm, identification and velocity measurement for driving vehicle are evaluated. this evaluated results is shown by hue data of vehicle passing of two virtual data sample lines, and the velocity measurement for driving vehicle is less than 0.4% comparing with existing vehicle speed meter system.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.634-642
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• 2004

Unlike actual vehicles, a vehicle driving simulator is limited in kinematic workspace and bounded on dynamic characteristics. So it is difficult to simulate dynamic motions of a multi-body vehicle model. In order to overcome these problems, a washout algorithm which controls the workspace of the simulator within the kinematic limitation is needed. However, a classical washout algorithm contains several problems such as generation of wrong sensation of motions by filters in tilt coordination, requirement of trial and error method in selecting the proper cut-off frequencies, difficulty in returning the simulator to its origin using only high pass filters and etc. This paper proposes a new tilt coordination method as an algorithm which gives more accurate sensations to drivers. In order to reduce time for returning the simulator to its origin, a new washout algorithm that the proposed algorithm selectively onset mode from high pass filters and return mode from error functions is proposed. As a result of this study, the results of the proposed algorithm are compared with the results of classical washout algorithm through the human perception models. Also, the performance of the suggested algorithm is evaluated by using human perception and sensibility of some drivers through experiments.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1271-1281
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• 2017

It is essential to collect and analyze target information around the vehicle for autonomous driving of the vehicle. Based on the analysis, environmental information such as location and direction should be analyzed in real time to control the vehicle. In particular, obstruction or cutting of objects in the image must be handled to provide accurate information about the vehicle environment and to facilitate safe operation. In this paper, we propose a method to simultaneously generate 2D and 3D bounding box proposals using LiDAR Edge generated by filtering LiDAR sensor information. We classify the classes of each proposal by connecting them with Region-based Fully-Covolutional Networks (R-FCN), which is an object classifier based on Deep Learning, which uses two-dimensional images as inputs. Each 3D box is rearranged by using the class label and the subcategory information of each class to finally complete the 3D bounding box corresponding to the object. Because 3D bounding boxes are created in 3D space, object information such as space coordinates and object size can be obtained at once, and 2D bounding boxes associated with 3D boxes do not have problems such as occlusion.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.142-147
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• 2013

Over the past few years, intelligent tire is developed very actively for more accurate measurement of real-time tire forces generated during vehicle driving situation. Information on the force of intelligent tire could be used very usefully to chassis control systems of a vehicle. Intelligent tire is based on deformation of tire's inner surface from the waveform of a SAW, or Surface Acoustic Wave. The tire vertical force is estimated by using variance analysis of sensor signals. The estimated tire vertical force is compared with the tire vertical force generated during vehicle driving situation in real-time environment. The scope of this paper is a correlation study between the measured sensor signals and the tire vertical force generated during vehicle driving situation.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.2
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• pp.72-77
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• 2011

In this paper, we propose effective diagnosis algorithm for hub bearing fault in driving vehicle using acceleration signal and wheel speed signal measured in hub bearing unit or knuckle. This algorithm consists of differential, envelope and power spectrum method. We developed diagnosis system for realizing proposed algorithm. This system consists of input device including acceleration sensor and wheel speed sensor, calculation device using Digital Signal Processor (DSP) and display device using Personal Digital Assistant (PDA). Using this diagnosis system, a driver can see hub bearing fault(flaking) from the vibration in driving vehicle. With early repairing, he can keep good ride feeling and prevent accident of vehicle resulting from hub bearing fault.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.4
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• pp.86-92
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• 2009

A driver-vehicle model means the integrated dynamic model that is able to estimate the steering wheel angle from the driver's desired path based on the dynamic characteristics of the driver and vehicle. Autonomous driving robot for factory automation has individual four-wheels which are driven by electronic motors. In this paper, the dynamic characteristics of several four-wheel steering systems with the simultaneously steerable front and rear wheels are investigated and compared by means of the driver-vehicle model. A diver-vehicle model is proposed by using the PID control to velocity and trajectory of control autonomous driving robot. To determine the optimum speed of a autonomous driving robot, steady-state circle simulation is carried out with the ADAMS program and MATLAB control model.

• Journal of ILASS-Korea
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• v.19 no.1
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• pp.40-46
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• 2014

Recently rise in oil prices feet the burden on not only diesel vehicle driver but also LPG vehicle driver, and get interested in various way to reduce fuel costs. In this study discuss on exhaust emissions characteristics on driving cycle mode and ignition advance condition change of CNG/LPLI Bi-Fuel vehicle. Experimental test was performed by changing the conditions of fuel (LPG/CNG), spark advance (Base, $10^{\circ}CA$, $15^{\circ}CA$), and driving mode (FTP-75, HWFET, and NEDC). In case of CO emission, in the order of CNG Base, CNG S/A10, S/A15 condition are average reduced -21%, -35%, -29% respectively compared to LPG fuel. The active emission reduction from the initial engine start, spark retard is likely to be beneficial in catalyst warm-up and improve combustion stability rather than spark advance.