• Journal of Information Processing Systems
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• v.19 no.5
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• pp.663-672
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• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2147-2152
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• 2011

This study of bimodal integration management system in conjunction with the tram and the tram cars bimodal integrated management system that occupies a part of the system to perform its role as a bimodal tram vehicle configuration, a device for the vehicle's infrastructure ryureul development and it is aimed to build on the vehicle. Bimodal tram vehicle infrastructure systems, internal and external information of the larger vehicles, and vehicles used to collect information for its own part and the integrated operations management center, or providing partial information from the station and collect/provide for the transfer of information to the communication part consists In this study, the core of these devices, the configuration of the vehicle infrastructure systems for the overall management and control of vehicles operating a computer's central processing device, vehicle infrastructure systems that make it manages and stores all jangchiryu Integrated Operations Management Center is reporting. In addition, seamless integration with operational management center for interactive communication in a vehicle mounted communications devices to maintain the best condition to manage. Current general traffic management system in a similar terminal device being used, but bimodal tram vehicles operating the computer of the vehicle operates the infrastructure to configure the devices around the one to configure the system in terms of step enhanced the active type, the operating terminal unit of inter active type. In this study, considering the future alignment of the accounting fee system, the expansion of the system reliability and stability around the activities that are underway.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.66-69
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• 2010

Lately, vehicle combined vehicle and IT(Information Technology) for vehicle's safety and convenience. so, vehicles is equipped with many ECU(Electronic control unit). the ECU's transmit data about each electronic control unit with OBD(On-Board Diagnostics) Network and data about each multimedia with MOST(Media Oriented System Transport) Network. In this paper, Supplementing disadvantage of existing blackbox, Using MOST of in-vehicle multimedia network and OBD-II of in-vehicle control network, blackbox system obtain the vehicle's driving state data. so, blackbox system judge vehicle's driving state and provide vehicle's driving state information to driver. Blackbox system implement the features mentioned above. as a result, blackbox is going to be more accurate blackbox system.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.253-256
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• 2013

For the reliable delivery of safety-related information to vehicles in the VANET, a reliable VANET routing protocol is required. In this paper, we propose a routing protocol that works based on the road vehicle density information for fast and reliable communications among vehicles within the city environment VANET. In the proposed mechanism, each vehicle computes the road vehicle density by using beacon messages and the road information. Based on the road vehicle density information, each vehicle establishes a reliable route for packet delivery. Through the NS-2 based simulations, we compare our proposed mechanism with GPSR and show that our mechanism outperforms GPSR in terms of packet delivery success rate.

• Journal of information and communication convergence engineering
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• v.4 no.3
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• pp.123-129
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• 2006

The limited number of roads and the increasing number of vehicles demand the automatic regulation of overspeed vehicles, illegal vehicles, and overloaded vehicles and the automatic charge calculation depending on the type of the vehicle. To meet such requirements, it is important to remove the shadow of the vehicle as processing and recognizing an image captured by a camera. The shadow of the vehicle is likely to cause misclassification of the vehicle type due to diverse errors and mistakes occurring when detecting geometrical properties of the vehicle. In case that shadows of two different vehicles are overlapped, not only the type of the vehicles may be misclassified but also it is difficult to accurately identify the type of the vehicles. In this paper, we propose a robust algorithm to remove the shadow of a vehicle by calculating the luminance, the chrominance, the gradient density of the cast shadow from information acquired using the image subtraction of the background, and to recognize the substantial vehicle figure. Even when it is hard to detect and split a target vehicle from its shadow as shadows of vehicles are attached to each other, our robust algorithm can detect the vehicle figure only. We implemented our system with a general camera and conducted experiments on various vehicles on general roads to find out our vehicle shade removal algorithm is efficient when detecting and recognizing vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1795-1811
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• 2019

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.

• 한국디지털정책학회:학술대회논문집
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• 2004.11a
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• pp.185-202
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• 2004

Recent development in wireless computing and GPS technology cause the active development in the application system of location information in real-time environment such as transportation vehicle management, air traffic control and location based system. Especially, study about vehicle location tracking system, which monitors the vehicle's position in a control center, is appeared to be a representative application system. However, the current vehicle location tracking system can not provide vehicle position information that is not stored in a database at a specific time to users. We designed a vehicle location tracking system that could track vehicle location using mobile interface such as PDA. The proposed system consist of a vehicle location retrieving server and a mobile interface. It is provide not only the moving vehicle's current location but also the position at a past and future time which is not stored in database for users.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.71-80
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• 2012

This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.679-691
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• 2016

As vehicle started to contain many different communication devices, collecting external information became possible in IoT environment. In such environment, remotely controling vehicle is possible when vehicle information is obtained by looking in to vehicle network through smart device. However, android based smart device applications are vulnerable to malicious modulation and redistribution. Modulated android application can lead to vehicle information disclosure that could bring about vehicle control accident which becomes threat to drivers. furthermore, since vehicles today does not contain security methods to protect it, they are very vulnerable to security threats which can cause serious damage to users and properties. In this paper, many different vehicle management android applications that are sold in Google Play has been analyzed. With this information, possible threats that could happen in vehicle management applications are being analysed to prove the risks. the experiment is done on actual vehicle to prove the risks. Also, access control method to protect the vehicle against malicious actions that could happen through external network in IoT environment is suggested in the paper.