Abstract: The invention provides a verification method of the dynamic virtual image display distance of a user interface, comprising the following steps: creating a tested image database; wherein the tested image database comprises a tested image displayed according to a standard virtual image display distance; displaying a first tested image; projecting a first image on a stacked image element; wherein the first image is displayed at a first virtual image display distance, which is the same with a first standard virtual image display distance of the first tested image; capturing the first tested image and the first image; performing a first reliability evaluation procedure for the first image and the first tested image; and calculating a first overlap ratio for the first image and the first tested image to verify accuracy of the user interface.

Abstract: A localization and mapping method is for localizing and mapping a moving apparatus in a moving process. The localization and mapping method includes an image capturing step, a feature point extracting step, a flag object identifying step, and a localizing and mapping step. The image capturing step includes capturing an image frame at a time point of a plurality of time points in the moving process by a camera unit. The flag object identifying step includes identifying whether the image frame includes a flag object among a plurality of the feature points in accordance with a flag database. The flag database includes a plurality of dynamic objects, and the flag object is corresponding to one of the dynamic objects. The localizing and mapping step includes performing localization and mapping in accordance with the image frames captured and the flag object thereof in the moving process.

Abstract: A localization and mapping method is for localizing and mapping a moving apparatus in a moving process. The localization and mapping method includes an image capturing step, a feature point extracting step, a flag object identifying step, and a localizing and mapping step. The image capturing step includes capturing an image frame at a time point of a plurality of time points in the moving process by a camera unit. The flag object identifying step includes identifying whether the image frame includes a flag object among a plurality of the feature points in accordance with a flag database. The flag database includes a plurality of dynamic objects, and the flag object is corresponding to one of the dynamic objects. The localizing and mapping step includes performing localization and mapping in accordance with the image frames captured and the flag object thereof in the moving process.

Abstract: A method for analyzing a number of people includes an image shooting step and a front-end analyzing step. In the image shooting step, a first image and a second image are obtained. In the front-end analyzing step, a foreground object analysis is operated, and a plurality of foreground objects located at a region of interest in the first image are obtained. A human body detection is operated, and at least one human body and a location thereof of the second image are obtained. An intersection analysis is operated, and the location of the human body is matched to the first image. A number of people estimation is operated to estimate the number of the people according to the first covering ratio, a number of the human body, and a second covering ratio of all the foreground objects to the region of interest.

Abstract: A method for analyzing a number of people includes an image shooting step and a front-end analyzing step. In the image shooting step, a first image and a second image are obtained. In the front-end analyzing step, a foreground object analysis is operated, and a plurality of foreground objects located at a region of interest in the first image are obtained. A human body detection is operated, and at least one human body and a location thereof of the second image are obtained. An intersection analysis is operated, and the location of the human body is matched to the first image. A number of people estimation is operated to estimate the number of the people according to the first covering ratio, a number of the human body, and a second covering ratio of all the foreground objects to the region of interest.

Abstract: A nighttime vehicle detecting method is for capturing an image by a camera and driving a computing unit to compute the image and then detect a highlight point of the image. The nighttime vehicle detecting method is for driving the computing unit to perform a communicating region labeling algorithm to label a plurality of highlight pixels connected to each other as a communicating region value, and then performing an area filtering algorithm to analyze an area of the highlight pixels connected to each other and judge whether the highlight pixels connected to each other are a vehicle lamp or not according to a size of the area. The nighttime vehicle detecting method is for driving the computing unit to perform an optical flow algorithm to obtain a speed of the vehicle lamp, and then filtering the vehicle lamp moved at the speed smaller than a predetermined speed.

Abstract: A lane tracking method is proposed for use by an autonomous vehicle running on a lane. A future location of the autonomous vehicle that corresponds to a future time point is estimated based on a current location and a measurement result of an inertial measurement unit of the autonomous vehicle. The future location of the autonomous vehicle, and a reference lane line data and a reference past location that correspond to a reference past time point are used to estimate a future lane line data that corresponds to the future time point.

Abstract: A lane tracking method is proposed for use by an autonomous vehicle running on a lane. A future location of the autonomous vehicle that corresponds to a future time point is estimated based on a current location and a measurement result of an inertial measurement unit of the autonomous vehicle. The future location of the autonomous vehicle, and a reference lane line data and a reference past location that correspond to a reference past time point are used to estimate a future lane line data that corresponds to the future time point.

Abstract: A vehicle lateral control system having a lane model with modulation weighting for controlling a vehicle includes a camera, an image processing device, a controller and a steering device. The camera is configured to capture a front image of the vehicle to generate a front image dataset. The front image dataset is analyzed by the image processing device to obtain a plurality of lane markers, and the image processing device establishes a lane fitting curve according to the lane markers and a target weighting. The controller has a plurality of vehicle dynamic parameters and a target distance. The target weighting is changeable according to the target distance. The controller generates a steering control weighting according to the lane fitting curve and the vehicle dynamic parameters. The steering device is configured to control a turning direction of the vehicle according to the steering control weighting.

Abstract: A nighttime vehicle detecting method is for capturing an image by a camera and driving a computing unit to compute the image and then detect a highlight point of the image. The nighttime vehicle detecting method is for driving the computing unit to perform a communicating region labeling algorithm to label a plurality of highlight pixels connected to each other as a communicating region value, and then performing an area filtering algorithm to analyze an area of the highlight pixels connected to each other and judge whether the highlight pixels connected to each other are a vehicle lamp or not according to a size of the area. The nighttime vehicle detecting method is for driving the computing unit to perform an optical flow algorithm to obtain a speed of the vehicle lamp, and then filtering the vehicle lamp moved at the speed smaller than a predetermined speed.

Abstract: An operation method of an adaptive driving beam headlamp system includes: in response to receipt of a sequence of images, when it is determined that a nearby vehicle is in the sequence of images, determining whether the nearby vehicle is in a first condition moving in a direction opposite to a moving direction of a driving vehicle and approaching the driving vehicle, or is in a second condition moving ahead of the driving vehicle in the moving direction of the driving vehicle; calculating a area into which the nearby vehicle is projected to move based on whether the nearby vehicle is in the first or the second condition; and controlling a headlamp to dim a part of a high beam pattern that corresponds with the area.

Abstract: A vehicle lateral control system having a lane model with modulation weighting for controlling a vehicle includes a camera, an image processing device, a controller and a steering device. The camera is configured to capture a front image of the vehicle to generate a front image dataset. The front image dataset is analyzed by the image processing device to obtain a plurality of lane markers, and the image processing device establishes a lane fitting curve according to the lane markers and a target weighting. The controller has a plurality of vehicle dynamic parameters and a target distance. The target weighting is changeable according to the target distance. The controller generates a steering control weighting according to the lane fitting curve and the vehicle dynamic parameters. The steering device is configured to control a turning direction of the vehicle according to the steering control weighting.

Abstract: A system for detecting a lane line of a road and a method thereof is disclosed. An image-retrieving device retrieves an image in front of a vehicle to generate a picture, and transmits the picture to a processing device. The processing device transforms one or two lane lines on the picture into a plurality of characteristic values, processes the characteristic values to generate one or two prediction lane lines, and uses them to dynamically predict and complete the lane lines, thereby forming a lane line prediction model. The processing device uses the lane line prediction model to determine whether the vehicle deviates, and transmits the picture and the lane line prediction model to a display device to display. When the vehicle deviates, the display device warns. The present invention can improve to recognize an unclear lane line, a single lane line and a far lane line.

Abstract: A system for detecting a lane line of a road and a method thereof is disclosed. An image-retrieving device retrieves an image in front of a vehicle to generate a picture, and transmits the picture to a processing device. The processing device transforms one or two lane lines on the picture into a plurality of characteristic values, processes the characteristic values to generate one or two prediction lane lines, and uses them to dynamically predict and complete the lane lines, thereby forming a lane line prediction model. The processing device uses the lane line prediction model to determine whether the vehicle deviates, and transmits the picture and the lane line prediction model to a display device to display. When the vehicle deviates, the display device warns. The present invention can improve to recognize an unclear lane line, a single lane line and a far lane line.