Abstract: A lane stripe detecting method based on a three-dimensional LIDAR is for detecting a lane stripe of a road surface around a vehicle. A data acquisition transforming step is for obtaining a plurality of vehicle scan point coordinates. The vehicle scan point coordinates are divided into a plurality of scan lines, and each of the scan lines has a plurality of scan points. A horizontal layer lane stripe judging step is judging whether each of the scan points is a horizontal stripe point or a non-horizontal stripe point according to a threshold value of each of the scan lines. At least two of the threshold values of the scan lines are different from each other. A vertical layer lane stripe judging step is for judging whether each of the horizontal stripe points is a same lane stripe point or a different lane stripe point.

Abstract: An active safety assistance system for pre-adjusting speed and a control method using the same detect whether there is one other vehicle around a host vehicle. The trajectory of the other vehicle neighboring the host vehicle is estimated when the other vehicle exists. After fitting the other-vehicle trajectory to a lane to determine the intention of the other vehicle, the method determines whether the other vehicle is used as a target vehicle that influences the movement of the host vehicle and calculates a control parameter according to the fitted result and the intention of the other vehicle. The method calculates a target speed and a steering-wheel angle of the host vehicle and controls a steering wheel, a throttle pedal and a brake force of the host vehicle according to the trajectory, the control parameter, and the target speed of the host vehicle.

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 stripe detecting method based on a three-dimensional LIDAR is for detecting a lane stripe of a road surface around a vehicle. A data acquisition transforming step is for obtaining a plurality of vehicle scan point coordinates. The vehicle scan point coordinates are divided into a plurality of scan lines, and each of the scan lines has a plurality of scan points. A horizontal layer lane stripe judging step is judging whether each of the scan points is a horizontal stripe point or a non-horizontal stripe point according to a threshold value of each of the scan lines. At least two of the threshold values of the scan lines are different from each other. A vertical layer lane stripe judging step is for judging whether each of the horizontal stripe points is a same lane stripe point or a different lane stripe point.

Abstract: The lidar detection device uses four two-dimensional lidars to scan an obstacle, to obtain original point-cloud data corresponding to the obstacle, and the original point-cloud data includes a relative distance, a relative angle and a relative speed of the obstacle relative to the vehicle, Next, the point-cloud data is classified into at least one point-cloud group corresponding to the obstacle, and a border length of the obstacle is obtained according to a contour of the point-cloud group. Kalman filter and extrapolation are used to estimate and track a movement path of a dynamic obstacle, and transmit the relative speed and the border length of the dynamic obstacle to an automatic driving controlling device. According to the relative distance, a coordinate of the dynamic obstacle nearest the vehicle can be obtained and transmitted to the automatic driving controlling device efficiently.

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 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 method for detecting driving behavior and a system using the same is disclosed. Firstly, at least one user image comprising an arm image and a head behavior image is retrieved. Then, a processor retrieves the arm image to compare with arm sample images stored in a database. The arm image corresponds to the arm sample image to determine a normal driving behavior. The arm image does not correspond to the arm sample image to determine an abnormal driving behavior, whereby the processor retrieves the head behavior image to compare with head regulation-violating sample images stored in the database. The head behavior image does not correspond to the head regulation-violating sample image to determine a normal driving behavior. The head behavior image corresponds to the head regulation-violating sample image to determine an abnormal driving behavior and output a second-level warning signal, thereby warning a driver.

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 method for detecting driving behavior and a system using the same is disclosed. Firstly, at least one user image comprising an arm image and a head behavior image is retrieved. Then, a processor retrieves the arm image to compare with arm sample images stored in a database. The arm image corresponds to the arm sample image to determine a normal driving behavior. The arm image does not correspond to the arm sample image to determine an abnormal driving behavior, whereby the processor retrieves the head behavior image to compare with head regulation-violating sample images stored in the database. The head behavior image does not correspond to the head regulation-violating sample image to determine a normal driving behavior. The head behavior image corresponds to the head regulation-violating sample image to determine an abnormal driving behavior and output a second-level warning signal, thereby warning a driver.

Abstract: A dynamic lane line detection system and method is disclosed herein. Four wide-angle lenses are respectively arranged at four sides of a vehicle and capture distorted images. A vehicular image integration unit transforms the distorted images into an ordinary planar image and presents the planar image on an image display device together with an image of lane lines as information of lane departure warning for the driver. The system undertakes moving obstacle detection, around view monitor, or parking assistant according to images captured by four wide-angle lenses and the gearshift signal, speed signal, turning angle signal received by a vehicular signal receiving device. The present invention uses the wide-angle lenses and the vehicular image integration unit to effectively integrate lane departure information with a panoramic image, whereby to decrease the economical burden of the vehicle buyer and reduce the volume of hardware.

Abstract: A self-adaptive image edge correction device and method thereof, including an image fetching unit, an image processing unit, and an image output unit. Wherein, the image fetching unit is used to provide an original image, and is connected electrically to the image processing unit, that includes a sharpening filter, a superimposer, and an edge detector. The sharpening filter converts the original image into a sharpened edge image, and the superimposer superimposes the original image to the sharpened edge image, to form an enhanced image. The edge detector fetches the edge of the enhanced image, to obtain a differential edge image. The image processing unit then utilizes selectively the horizontal correction or vertical correction to correct the differential edge image, based on s deviation direction of the differential edge image, to form a corrected image, and provide it to the image output unit to output as required.

Abstract: A dynamic lane line detection system and method is disclosed herein. Four wide-angle lenses are respectively arranged at four sides of a vehicle and capture distorted images. A vehicular image integration unit transforms the distorted images into an ordinary planar image and presents the planar image on an image display device together with an image of lane lines as information of lane departure warning for the driver. The system undertakes moving obstacle detection, around view monitor, or parking assistant according to images captured by four wide-angle lenses and the gearshift signal, speed signal, turning angle signal received by a vehicular signal receiving device. The present invention uses the wide-angle lenses and the vehicular image integration unit to effectively integrate lane departure information with a panoramic image, whereby to decrease the economical burden of the vehicle buyer and reduce the volume of hardware.

Abstract: An automatic airview correction method comprises steps: moving a vehicle to an airview alignment pattern; capturing a plurality of airview alignment images of the surroundings of the vehicle; correcting distortion of the airview alignment images to obtain a plurality of corrected images; performing alignment compensation on the corrected images; searching for corner points of the corrected images and converting view points to obtain a plurality of view angle-converted images; and searching for corner points of the view angle-converted images, and seaming the view angle-converted images to form a panoramic airview and obtain parameters corresponding to the panoramic airview. The present invention can automatically align images and can also automatically detect corner points to seam the images of the surroundings of a vehicle, whereby to form a panoramic airview.

Abstract: A self-adaptive image-based obstacle detection method comprises steps: capturing an original image; transforming the original image to an HSV color space, and retrieving a hue component (H) and a saturation component (S) of the HSV color space to form an HS-based image; dividing the HS-based image into image blocks; selecting one image block as a background block; using an obstacle recognition equation to determine whether each of the image blocks is similar to the background block; if no, deleting the image block; if yes, preserving the image block to form a binary obstacle image; and overlaying the binary obstacle image on the original image to filter out the background and obtain an initial ambit of an obstacle image. Then, three orderly movement flow equations are used to determine whether it is an obstacle.

Abstract: A self-adaptive image edge correction device and method thereof, including an image fetching unit, an image processing unit, and an image output unit. Wherein, the image fetching unit is used to provide an original image, and is connected electrically to the image processing unit, that includes a sharpening filter, a processor, and an edge detector. The sharpening filter converts the original image into a sharpened edge image, and the processor superimposes the original image to the sharpened edge image, to form an enhanced image. The edge detector fetches the edge of the enhanced image, to obtain a differential edge image. The processor then utilizes selectively the horizontal correction or vertical correction to correct the differential edge image, based on s deviation direction of the differential edge image, to form a corrected image, and provide it to the image output unit to output as required.

Abstract: An automatic airview correction method comprises steps: moving a vehicle to an airview alignment pattern; capturing a plurality of airview alignment images of the surroundings of the vehicle; correcting distortion of the airview alignment images to obtain a plurality of corrected images; performing alignment compensation on the corrected images; searching for corner points of the corrected images and converting view point s to obtain a plurality of view angle-converted images; and searching for corner points of the view angle-converted images, and seaming the view angle-converted images to form a panoramic airview and obtain parameters corresponding to the panoramic airview. The present invention can automatically align images and can also automatically detect corner points to seam the images of the surroundings of a vehicle, whereby to form a panoramic airview.

Abstract: A self-adaptive image-based obstacle detection method comprises steps: capturing an original image; transforming the original image to an HSV color space, and retrieving a hue component (H) and a saturation component (S) of the HSV color space to form an HS-based image; dividing the HS-based image into image blocks; selecting one image block as a background block; using an obstacle recognition equation to determine whether each of the image blocks is similar to the background block; if no, deleting the image block; if yes, preserving the image block to form a binary obstacle image; and overlaying the binary obstacle image on the original image to filter out the background and obtain an initial ambit of an obstacle image. Then, three orderly movement flow equations are used to determine whether it is an obstacle.