The public perspective on renewable energy versus nuclear power for carbon neutrality in South Korea.

South Korea has legislated "2050 carbon neutrality" in 2021 and is currently implementing it, and debate is brewing over which to focus on as the main means of achieving it in the power generation sector: renewable energy (RE) or nuclear power (NP). This article aims to collect and analyze data on the public preference for RE versus NP. In a national survey of 1000 people, respondents were first asked which was preferred, RE or NP, and then asked to indicate the preference intensity along a 5-point scale. Of all the respondents, 60.3% preferred RE and 27.7% preferred NP. The preference for the former was about 2.2 times more than that for the latter. However, the intensity of the preference for NP was 1.3 times more than that for RE. Both the two-limit Tobit model and ordered probit model have been applied to analyzing the factors influencing the preference. The effects of some variables on the preference for RE over NP are explained, and implications from this are discussed. The findings can be used as a reference to determine the main means of implementation of carbon neutrality or to increase the public acceptance of the specified means.

Extended Line Map-Based Precise Vehicle Localization Using 3D LIDAR.

An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively.

Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area.

Tall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by using the light detection and ranging (LIDAR) sensor. A vertical corner feature based precise vehicle localization method is proposed in this paper and implemented using 3D LIDAR (Velodyne HDL-32E). The vehicle motion is predicted by accumulating the pose increment output from the iterative closest point (ICP) algorithm based on the geometric relations between the scan data of the 3D LIDAR. The vertical corner is extracted using the proposed corner extraction method. The vehicle position is then corrected by matching the prebuilt corner map with the extracted corner. The experiment was carried out in the Gangnam area of Seoul, South Korea. In the experimental results, the maximum horizontal position error is about 0.46 m and the 2D Root Mean Square (RMS) horizontal error is about 0.138 m.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.