Flash visual evoked potential (FVEP), induced by OFF-to-ON flash, i.e. flash onset, in a light emitting diode (LED) was used to control four cursor movements (left, right, up, down), and left-and rightbutton clicks on a screen menu. ON or OFF duration in each flashing sequence was designed to be random so that all flashing sequences were mutually independent. Since FVEPs are time-locked and phaselocked to flash onsets of gazed LEDs, segmenting EEG signals based on the flash onsets of each flashing sequence followed by averaging will sharpen epochs evoked by gazed LEDs. Four inexperienced subjects were asked to generate a sequence of cursor commands. Mean recognition and transfer rates were 88% and 3.74 s=command, respectively.

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Abstract. As research on autonomous driving deepens, High-definition Maps (HD Maps) have gradually become an auxiliary information for the new generation of autonomous driving technology. Compared to traditional electronic navigation maps, HD Maps have higher accuracy requirements and more information. Multi-road environment information and road elements are included. In the production of HD Maps, the on-board Mobile Laser Scanning (MLS) system has the ability to quickly collect environmental information, with high precision, thus making the system a widely used data collection method today. However, subsequent map building, digitization, and other mapping work still rely on manual operation, which is time-consuming and laborious. Therefore, this research is dedicated to developing a semi-automatic algorithm to generate HD Maps from the acquired point cloud data. This research focuses on the extraction of road surface markings, using the Cloth Simulation Filter (CSF) to obtain the road surface point cloud to improve the extraction efficiency. The road markings are extracted using the characteristic of high intensity values, and the commonly used Otsu threshold filter in image processing is used to extract point clouds with high reflectance intensity, eliminating the need for manual setting of point clouds. And based on geometric conditions, the objects are classified, such as arrow lines, pedestrian crossings, stop lines, and lane lines, which are convenient for further mapping HD Maps.

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