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Vehicle localization is essential for autonomous driving. Basically, the position information of the autonomous vehicle is obtained from the Global Positioning System (GPS). More accurate localization can be performed by using maps and various sensors that are mounted on the autonomous vehicle. GPS receivers cannot receive GPS signals in tunnels, so dead reckoning (DR) is used for vehicle localization. However, the error from DR continuously accumulates. Therefore, this error must be corrected by using vehicle-mounted sensors, such as Light Detection and Ranging (LIDAR) and cameras. Tunnels have very specific shape information, which is usually an ellipse, and several emergency facilities exist in tunnels. Some facilities are separated from the tunnel wall, which can be detected by using 3D LIDAR. In particular, fire extinguisher lamps are periodically installed at intervals of 50 m, which can serve as good landmarks. First, the point cloud for the tunnel wall must be removed to effectively detect fire extinguisher lamps. This process can be easily conducted by using shape information. After this removal, we detect the fire extinguisher lamps. In this paper, we propose a 3D LIDAR-based vehicle-localization method that uses ellipse parameters and fire extinguisher lamps in road tunnels. These experiments are conducted at the Maseong tunnel in South Korea. The experimental results show that the root mean square (RMS) position errors in the lateral and longitudinal directions were 0.06 m and 0.23 m, respectively, exhibiting precise localization accuracy.
The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.
In this paper, a detection method of a pedestrian safety road marking was proposed. The proposed algorithm uses laser range and reflectivity of a range finder (LRF). For a detection of crosswalk marking and stop line, the DFT (Discrete Fourier Transform) of reflectivity and cross-correlation method between the reference replica and the measured reflectivity are used. A speed bump is detected through measuring an altitude difference of two LRFs which have the different tilted angle. Furthermore, we proposed a velocity constrained a detection method of a speed bump. Finally, the proposed methods are tested in on-line, on the pavement of a road. The considered road markings are wholly detected. The localization errors of both road markings are smaller than 0.4 meter.
In this paper, spoofing effects on a GNSS receiver were analyzed. The spoofer (spoofing device) was classified to two categories. One is an active spoofer and the other is a passive spoofer. The active spoofer was considered for analysis. For the analysis of spoofing effects on a GNSS receiver, a real-time GNSS spoofing simulator was developed. The simulator was consisted with two parts which are a baseband signal generation part and a RF up-conversion part. The first GNSS baseband signal was generated according to spoofing parameters such as range, range rate, GNSS navigation data, spoofing to GNSS signal ratio, and etc. The generated baseband signal was up-converted to GNSS L1 band. Then the signal transmitted to a GNSS signal. For a perfect spoofing, a spoofer knew an accurate position and velocity of a spoofing target. But, in real world, that is not nearly possible. Although uncertainty of position and velocity of the target was existed, the spoofer was operated as an efficient jammer.
Eighteen specimens of juvenile Mugilidae were collected in October 2012 from the southern coastal waters of Jeju Island, and identified based on analysis of their mitochondrial DNA 16S rRNA sequences. Seventeen specimens of Oedalechilus labiosus and a single specimen of Ellochelon vaigiensis were found, constituting a new record for these species among Korean ichthyofauna. O. labiosus is identified by the angle at the posterior end of its mouth, which contains a round notch, a darkish dorsal margin of the pectoral fin, the presence of 33-36 lateral line scales, and 23-24 vertebrae. E. vaigiensis is identified by dark dorsal and pectoral fins, the presence of 26 lateral line scales, and 25 vertebrae. The proposed Korean name for Oedalechilus is ``Sol-ip-sung-eo-sok`` and that for Ellochelon is ``Nup-jeok-ggo-ri-sung-eo-sok.`` The proposed Korean names for the species are ``Sol-ip-sung-eo`` and ``Nup-jeok-ggo-ri-sung-eo`` for O. labiosus and E. vaigiensis, respectively. We present a key for identification of the Mugilidae family of species from Korea, and include these two newly recorded species.