• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.537-542
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• 2007

This paper proposes a 2-step Kalman filter model for land vehicle navigation using civilian-band GPS measurements. The velocity bias caused by the Earth#s rotation would be removed completely when applying this model. Because the linearization of velocity equations in this model is not necessary, the error is significantly reduced. The experiment reveals that estimated position error with stationary data is about 5m during a 15-20 minute interval. The other benefit of this model is that it can be feasibly applied as a GPS receiver module thanks to the small sizes of the necessary manipulating matrices.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.87-94
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• 2005

3D positional information of lane can be automatically calculated tv combining GPS data, IMU data if coordinates of lane centers are given. The Road Safety Survey and Analysis Vehicle(RoSSAV) is currently under development to analyze three dimensional safety and stability of roads. RoSSAV has GPS and IMU sensors to get positional information of the vehicle and two road-facing CCD cameras for extraction of lane coordinates. In this paper, we develop technology that automatically detects centers of lanes from the road-facing cameras of RoSSAV. The proposed algorithm defines line-support regions by grouping pixels with similar edge orientation and magnitude together and extracts a line from each line support region by planar fitting. Then if extracted lines and the region in-between satisfy the criteria of brightness and width, we decide this region as lane. The proposed algorithm was more precise and stable than the previously proposed algorithm based on brightness threshold method. Experiments with real road scenes confirmed that lane was effectively extracted by the proposed algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.531-537
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• 2010

Real-time Aerial Monitoring System performs the rapid mapping in an emergency situation so that the geoinformation could be constructed in near real time. In this system, the position and attitude information from GPS/INS integration algorithm is used to perform the aerial triangulation(AT) without GCPs. Therefore, if we obtain Exterior Orientation(EO) estimates from AT sequentially, EO are used as the measurements in the Kalman filter. In this study, we simulate the GPS/IMS/Image data for an UAV-based aerial monitoring system and compare the GPS/INS/AT with and without from AT. Comparative analysis showed that result from the GPS/INS/AT with EO update is more accurate than without the update. However, when the vehicle turns, the position error significantly increases which need more analysis in the future.

• Journal of Biosystems Engineering
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• v.36 no.2
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• pp.116-121
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• 2011

This study was conducted to develop a robust navigator which could be in positioning for precision farming through developing a plural GPS receiver with 4 sets of GPS antenna. In order to improve positioning accuracy by integrating GPS signals received simultaneously, the algorithm for processing plural GPS signal effectively was designed. Performance of the algorithm was tested using a simulation program and a fixed point on WGS 84 coordinates. Results of this study are aummarized as followings. 1. 4 sets of lower grade GPS receiver and signals were integrated by kalman filter algorithm and geometric algorithm to increase positioning accuracy of the data. 2. Prototype was composed of 4 sets of GPS receiver and INS components. All Star which manufactured by CMC, gyro compass made by KVH, ground speed sensor and integration S/W based on RTOS(Real Time Operating System)were used. 3. Integration algorithm was simulated by developed program which could generate random position error less then 10 m and tested with the prototype at a fixed position. 4. When navigation data was integrated by geometrical correction and kalman filter algorithm, estimated positioning erros were less then 0.6 m and 1.0 m respectively in simulation and fixed position tests.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.70-81
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• 2020

Point-by-point data, which is abundantly collected by vehicles with embedded GPS (Global Positioning System), generate useful information. These data facilitate decisions by transportation jurisdictions, and private vendors can monitor and investigate micro-scale driver behavior, traffic flow, and roadway movements. The information is applied to develop app-based route guidance and business models. Of these, speed data play a vital role in developing key parameters and applying agent-based information and services. Nevertheless, link speed values require different levels of physical storage and fidelity, depending on both collecting and reporting intervals. Given these circumstances, this study aimed to establish an appropriate collection interval to efficiently utilize Space Mean Speed information by vehicles with embedded GPS. We conducted a comparison of Probe-vehicle data and Image-based vehicle data to understand PE(Percentage Error). According to the study results, the PE of the Probe-vehicle data showed a 95% confidence level within an 8-second interval, which was chosen as the appropriate collection interval for Probe-vehicle data. It is our hope that the developed guidelines facilitate C-ITS, and autonomous driving service providers will use more reliable Space Mean Speed data to develop better related C-ITS and autonomous driving services.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1729-1734
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• 2007

Recently, as the feasibility study shows that trans-Korea railway and trans-continental railway are advantageous, interest in high-speed railway system is increasing. Because railway vehicle is environment-friendly and safe compared with airplane and ship, its market-sharing increases gradually. We developed a measurement system for on-line test and evaluation of performances of KHST. The measurement system is composed of software part and hardware part. Perfect interface between multi-users is possible. Nowadays, position data inputs to pulse signal from wheel. Perfect position measurement was limited to slip and slide of vehicle. This measurement makes up for the weak points, Position Detection System using GPS develops. By using the system, Korean High Speed Train is capable of accurate fault position detection.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.802-810
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• 2006

For a thorough train control, the precise train position detection is necessarily required. The widely used current way for train position detection is the one of using track circuits. The track circuit has a simple structure, and has a high level of reliability. However trains can be detected only on track circuits, which have to be installed on all ground sections, and much amount of cost for its installation and maintenance is needed. In addition, for the track circuit, only discontinuous position detection is possible because of the features of the closed circuit loop configuration. As the recent advances in telecommunication technologies and high-tech vehicle-based control equipments, for the train position detection, the method to detect positions directly from on trains is being studied. Vehicle-based position detection method is to estimate train positions, speed, timing data continuously, and to use them as the control information. In this paper, the features of GPS navigation and DR navigation are analyzed, and the navigation filters are designed by constructing vehicle-based train position detection method by combining GPS navigation and DR navigation for their complementary cooperation, and by using kalman filter. The position estimation performance of the proposed method is also confirmed by simulations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.422-424
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• 2010

Recently the car attached many ECUs and entertainment devices to provide the easiness maintenance and driver's convenience. CAN and MOST networks have been used to manage and control those devices in the car. Wireless network also has been established to receive information from external. These days a car navigation system with GPS is being integrated with CAN, MOST and Wireless network. In these circumstances, the car navigation system can have HMI function to integrate and control the car networks' devices. To solve the GPS problems such as positioning errors or losing signals from satellites in the tunnels and urban canyons, this paper designs and implements a method for positioning vehicle by using the sensing data of sensors and Wi-Fi devices based on this integrated environment.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.