• International Journal of Automotive Technology
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• v.5 no.2
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• pp.95-108
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• 2004

Lanekeeping assistance has the potential to save thousands of lives every year by preventing accidental road departure. This paper presents experimental validation of a potential field lanekeeping assistance system with quantitative performance guarantees. The lanekeeping system is implemented on a 1997 Corvette modified for steer-by-wire capability. With no mechanical connection between the hand wheel and road wheels the lanekeeping system can add steering inputs independently from the driver. Implementation of the lanekeeping system uses a novel combination of a multi-antenna Global Positioning System (GPS) and precision road maps. Preliminary experimental data shows that this control scheme performs extremely well for driver assistance and closely matches simulation results, verifying previous theoretical guarantees for safety. These results also motivate future work which will focus on interaction with the driver.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.65-72
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• 2012

With the development of MEMS, small and low-priced sensors integrating IMU and GPS have produced and exploited for diverse field. In this research, we have judged that MEMS-based IMU/GPS sensor is suitable for light-weighted mobile mapping system and carried out experiments to analyze the characteristics of MTi-G, which was developed from XSens company. From a sensor which fixed to dashboard, coordinates results with no post-processing were achieved for test area. On the whole, the results show satisfactory performances but some errors also were discovered from parts of the road due to sensor properties, XKF characteristics and GPS reception environment. We could confirm the potential of light-weighted mobile mapping system. Experiments considering various GPS reception environments and road condition and more detailed level of accuracy analysis will be performed for further research.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1095-1109
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• 2021

The technology used to recognize the location and surroundings of autonomous vehicles is called SLAM. SLAM standsfor Simultaneously Localization and Mapping and hasrecently been actively utilized in research on autonomous vehicles,starting with robotic research. Expensive GPS, INS, LiDAR, RADAR, and Wheel Odometry allow precise magnetic positioning and mapping in centimeters. However, if it can secure similar accuracy as using cheaper Cameras and GPS data, it will contribute to advancing the era of autonomous driving. In this paper, we present a method for converging monocular camera with RTK-enabled GPS data to perform RMSE 33.7 cm localization and mapping on the urban road.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.252-263
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• 2016

Road sign is an important street furniture that gives some information such as road conditions, driving direction and condition for a driver. Thus, road sign is a major target of image recognition for self-driving car, ADAS(autonomous vehicle and intelligent driver assistance systems), and ITS(intelligent transport systems). In this paper, an enhanced road sign recognition system is proposed for MMS(Mobile Mapping System) using the single camera and GPS. For the proposed system, first, a road sign recognition scheme is proposed. this scheme is composed of detection and classification step. In the detection step, object candidate regions are extracted in image frames using hybrid road sign detection scheme that is based on color and shape features of road signs. And, in the classification step, the area of candidate regions and road sign template are compared. Second, a Geo-marking scheme for geo-content that is consist of road sign image and coordinate value is proposed. If the serious situation such as car accident is happened, this scheme can protect geographical information of road sign against illegal users. By experiments with test video set, in the three parts that are road sign recognition, coordinate value estimation and geo-marking, it is confirmed that proposed schemes can be used for MMS in commercial area.

• Spatial Information Research
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• v.13 no.1 s.32
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• pp.91-101
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• 2005

The Mobile Mapping System using the vehicle equipped the GPS, IMU, CCD Cameras is the effective system for the management of the road facilities, update of the digital map, and etc. If the geographic information which is acquired by the Mobile mapping System can be transmitted in realtime, users can process what they want using the latest data. In this research, the effective method was suggested for the transmission of the geographic information acquired by mobile mapping System such as position data, attitude data, and image data in the wireless internet environment in realtime.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.119-127
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• 2019

In this study, we investigated the application of deep learning to the manufacturing process of traffic and road signs which are constituting the road layer in map production with 1 / 1,000 digital topographic map. Automated classification of road traffic sign images was carried out through construction of training data for images acquired by using transfer learning which is used in image classification of deep learning. As a result of the analysis, the signs of attention, regulation, direction and assistance were irregular due to various factors such as the quality of the photographed images and sign shape, but in the case of the guide sign, the accuracy was higher than 97%. In the digital mapping, it is expected that the automatic image classification method using transfer learning will increase the utilization in data acquisition and classification of various layers including traffic safety signs.

• Journal of Power System Engineering
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• v.20 no.5
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• pp.37-45
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• 2016

Exhaust gas recirculation has the advantage of being low-cost and easy to control of NOx emission. Therefore, it is most generally used to reduce NOx emission according to strengthen regulation. In the case of a non-road engine, such as the agricultural engine, since it mainly operate a middle or high-load state, NOx emission is decreased in accordance with the mapping range of the EGR rate, but results in an increase in the particulate matter which is caused to deposit and fouling problem of the EGR system. This problem has become an important issue for maintaining the performance of the engine, as well as emission performance. This study had examined the effects of cooler aging on the performance of heat transfer efficiency and NOx emission in non-road diesel engine. As a result of the EGR cooler aging during 200 hours engine operation, the cooling performance decreased about 25% compared with that of fresh cooler and the NOx emission increased about 14.6% on NRSC(non-road steady cycle) and 20% on NRTC(non-road transient cycle) compared with that of fresh cooler respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.355-360
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• 2017

Recently, with the development of autonomous vehicle technology, the importance of precise road maps is increasing. A precise road map is a digital map with lane information, regulations, safety information, and various road facilities. Conventional precise road maps have been tested and developed based on the mobile mapping system (MMS). But they have not been activated due to high introduction costs. However, in the case of unmanned aerial vehicles (UAVs), the application field is continuously increasing. This study tries to extract information through classification of high-resolution UAV images for autonomous driving. Autonomous vehicle test roads were selected as study sites, and high-resolution orthoimages were produced using UAVs. In addition, the utilization of high-resolution orthoimages has been proposed by effectively extracting data for precise road map construction, such as road lines, guards, and machines through image classification. If additional experimentation and verification are performed, the field of UAV image use will be expanded, providing the data to automobile manufacturers and related public and private organizations, and venture companies will contribute to the development of domestic autonomous vehicle technology.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.102-116
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• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.