• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2730-2736
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• 2010

Recently, various networks have been introduced in the car of the internal and external sides. These have been integrated by one HMI(Human Machine Interface) to control devices of each network and provide information service. The existing vehicle navigation system, providing GPS based vehicle positioning service, has been included to these integrated networks as a default option. The GPS has been used to the most universal device to provide position information by using satellites' signal. But It is impossible to provide the position information when the GPS can't receive the satellites' signal in the area of tunnel, urban canyon, or forest canopy. Thus, this paper propose and implement the method of measuring vehicle position by using the sensing data of internal CAN network and external Wi-Fi network of the integrated car navigation circumstances when the GPS doesn't work normally. The results obtained by implementation shows the proposed method works well by map matching.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.80-87
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• 2006

In this paper, we analyze the performance of IEEE 802.11b WLAN communication between access point(AP) and mobile equipment(ME) in 2.4 GHz band with noise and interference factors. WLAN communication at in-vehicle environment is assumed as the communication between main vehicle controller and electronic device such as sensor, ECU (Electrical Control Unit) in vehicle on telematics field for implementing wireless vehicle control system. Received interference level from other system's mobile equipment in the same band and automobile noise from each part of vehicle can be the main factors that can cause increasing error rate of control signal. With these (actors, we focus on the Eb/No the BER performance of WLAN for analyzing the characteristic of interference factors by the measured bit error rate.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.21-29
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• 2015

For higher reliability against component failures in railroad trains with many electronic sensors and actuators, a distributed control system with which all electronic components are connected via a network is being considered. This paper compares and analyzes various topologies of Ethernet network for a railroad train in the aspects of (1) failure recovery, (2) the number of ports per device, (3) the number of cable connections between vehicles, and (4) performance. Especially, the unique characteristic of a train system that the number of vehicles changes is considered through analysis. Various combinations of in- and inter-vehicle topologies are considered. In addition, we introduce a hybrid of star and daisy-chain topology for inter-vehicle connection when the maximum number of inter-vehicle connections is limited to reduce possible failures of inter-vehicle connections. Simulation results show performance comparison between different topology combinations; the hybrid topology is shown to enhance delay performance even with a highly limited number of inter-vehicle connections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.144-155
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• 2019

One of the issues of the automotive industry today is autonomous driving vehicles. In order to achieve level 3 or higher as defined by SAE International, harmonization of autonomous driving technology and connected technology is essential. Current vehicles have new features such as autonomous driving, which not only increases the number of electrical components, but also the amount and complexity of software. As a result, the attack surface, which is the access point of attack, is widening, and software security vulnerabilities are also increasing. However, the reality is that the essential security requirements for vehicles are not defined. In this paper, based on real attacks and vulnerability cases and trends, we identify the assets in the in-vehicle network and derive the threats. We also defined the security requirements and derived essential security requirements that should be applied at least to the safety of the vehicle occupant through risk analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1399-1410
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• 2003

In this paper, we develop a joystick manual driving algorithm for an electric vehicle called Cycab. Cycab is developed as a public transportation vehicle, which can be driven either by a manual joystick or an automated driving mode. The vehicle uses six motors for driving four wheels, and front/rear steerings. Cycab utilizes one industrial PC with a real time Linux kernel and four Motorola MPC555 micro controllers, and a CAN network for the communication among the five processors. The developed algorithm consists of two automatic vehicle speed control algorithms for normal and emergency situations that override the driver's joystick command and an open loop torque distribution algorithm for the traction motors. In this study, the algorithm is developed using SynDEx, which is a system level CAD software dedicated to rapid prototyping and optimizing the implementation of real-time embedded applications on distributed architectures. The experimental results verify the usefulness of the two automatic vehicle control algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.584-591
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• 2012

Distributing a Certificate Revocation List (CRL) quickly to all vehicles in the system requires a very large number of road side units (RSUs) to be deployed. In reality, initial deployment stage of vehicle networks would be characterized by limited infrastructure as a result in very limited vehicle to infrastructure communication. However, every vehicle wants the most recent CRLs to protect itself from malicious users and malfunctioning equipments, as well as to increase the overall security of the vehicle networks. To address this challenge, we design and implement a nomadic device based CRL acquisition method using nomadic device's communication capability with cellular networks. When a vehicle could not directly communicate with nearby RSUs, the nomadic device acts as a security mediator to perform vehicle's security functions continuously through cellular networks. Therefore, even if RSUs are not deployed or sparsely deployed, vehicle's security threats could be minimized by receiving the most recent CRLs in a reasonable time.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.259-266
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• 2005

In this paper, we developed a vehicle condition monitoring system that checks vehicle conditions, and transmits and displays them to a driver for safety and effective maintenance. We used a CAN controller and transceiver to establish the CAN communication that has been used commonly inside an actual vehicle for the collection of vehicle's status information. To validate the operation of the developed system, we have confirmed the accuracy and stability of data transmission and reception of vehicle information.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.827-828
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• 2006

This paper presents a vehicle control algorithm for Personal Rapid Transit (PRT) system. PRT system is a one-way direction network system which is composed of guideway branches, merging/diverging points. Vehicle control algorithm can be divided into two kinds. Those are merging control algorithm and the other. We emphasized on the merging control algorithm. For that, we first devised a front/virtual front vehicle finding strategies. Properly determined front/virtual front vehicle is the starting point of vehicle control. The objects of merging control are to avoid collision and to pass the merging point fluently. Which implies that jerk constraint and limits of acceleration and deceleration etc. are should be considered. To verify the validation of the vehicle algorithm, we executed simulations and presented test results.