A method of processing an image of a road having a road marker acquired by a vehicle-mounted camera to generate boundary data indicating a boundary of the road marker region of the image which represents the road marker, comprising: generating an LL sub-band image of an M.sup.th level of an (M+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image M times; generating a sub-band image of an (M+1).sup.th level of the (M+1) level DWT decomposition by high-pass filtering the LL sub-band image and down-sampling a result of the high-pass filtering; and determining a boundary of a region of pixels of the sub-band image of the (M+1).sup.th level, the region being surrounded by pixels having pixel values substantially different to the pixel values of the pixels in the region, the determined boundary indicating the boundary of the road marker region.

Systems and methods for determining the amount of time that an object occupies a predetermined space are provided. In one implementation, a system comprises a sensor configured to determine a start time when a recognizable object enters a predetermined space. The sensor is further configured to determine an end time when the recognizable object leaves the predetermined space. The system also includes a processing device configured to determine an elapsed time, based on the start time and the end time, when the recognizable object remains in the predetermined space.

An apparatus for determining a speed of a vehicle along a road by processing a first image and a second image of the road captured by a camera on the vehicle and comprising respective road marker images of a road marker, the apparatus arranged to: determine a location of the road marker in the first image; predict a location of the road marker in the second image based on the determined location, an estimate of the vehicle speed, and a time period between capture of the images; detect the road marker in a portion of the second image at the predicted location; estimate a distance moved by the vehicle during the time period based on the determined location, and a location of the detected road marker in the portion of the second image; and calculate the speed based on the estimated distance and the time period.

An apparatus for recognizing a division line on a road from an image captured by a camera includes: a processing area setting unit to set a processing area to the image; a statistics calculation unit to calculate statistics of the image in the processing area; a threshold value setting unit to set a plurality of threshold values on the basis of the statistics; a division line feature point extraction unit to classify a plurality of pixels contained in the image on the basis of the white line threshold value and the road surface threshold value and extracts feature points of the division line on the basis of classification results of the plurality of pixels; and a division line decision unit configured to decide the division line on the basis of the feature points extracted by the division line feature point extraction unit.

Embodiments of the present invention disclose a map construction method, apparatus, and storage medium. The method includes the following steps: acquiring sensor data collected by a preset sensor; establishing a pose constraint relationship of a movable object and a semantic constraint relationship of a semantic object according to the sensor data; performing a joint optimization solution according to the pose constraint relationship and the semantic constraint relationship to determine a semantic result of the semantic object and a pose result of the movable object; and constructing a semantic map and a point cloud map according to the semantic result and the pose result. By adopting the above technical solution, the semantic map and the point cloud map can be obtained quickly and accurately, thereby obtaining a more accurate high-definition map.

Embodiments of the invention provide an apparatus for detecting a state of a road surface. The apparatus includes an input interface, an image divider, a parameter calculator, and a classifier. The input interface is configured to obtain recording information of the road surface, such as a camera recording. The image divider is configured to divide the recording information into a plurality of windows, wherein each window includes a plurality of image elements, and wherein each image element includes at least two different pieces of information, such as a spectral absorption of the road surface and/or a polarization of the reflected light. The parameter calculator is configured to calculate at least two parameters per window by using the at least two different pieces of information of each image element in the window. The classifier is configured to classify the window on the basis of a tuple of the at least two parameters of the window, and to detect, on the basis of the tuple of the at least two parameters of the window, as input values a state of the window and to output the state of the window as an output value.

A method and system for determining a geographical location and orientation of a vehicle travelling through a road network is disclosed. The method comprises obtaining, from one or more cameras associated with the vehicle travelling through the road network, a sequence of images reflecting the environment of the road network on which the vehicle is travelling, wherein each of the images has an associated camera location at which the image was recorded. A local map representation representing an area of the road network on which the vehicle is travelling is then generated using at least some of the obtained images and the associated camera locations. The generated local map representation is compared with a section of a reference map, the reference map section covering the area of the road network on which the vehicle is travelling, and the geographical location and orientation of the vehicle within the road network is determined based on the comparison. Methods and systems for generating and/or updating an electronic map using data obtained by a vehicle travelling through a road network represented by the map are also disclosed.

The present disclosure relates to a method and apparatus for generating training data of a deep learning model for lane classification. The method according to an embodiment of the present disclosure is performed by an electronic apparatus, and is a method for generating training data of a deep learning model for lane classification by generating a composite image of the other color lane using images of a white lane and the other color lane, and includes determining a ratio of other two channels based on one channel (reference color channel) for three color channels of red (R), green (G) and blue (B) of the other color lane in the image of the other color lane; and generating a composite image of the other color lane by scaling the image of the white lane by applying the determined ratio to the other two channels with respect to the reference color channel of the white lane.

A method of recognizing a surrounding driving environment based on a surround view mirror (SVM) original image. The method includes: acquiring an image captured by an SVM camera; semantically segmenting a pixel corresponding to a vehicle (hereinafter, a vehicle pixel) and a pixel corresponding to a lane (hereinafter, a lane pixel) from the image; post-processing the semantically segmented vehicle pixel and lane pixel to extract object information including adjacent lane information and adjacent vehicle information from the image; and converting the extracted object information into a physical position and transmitting the physical position to a driving controller.

The present disclosure relates to a traffic marker detection method and a training method for a traffic marker detection model, and relates to the technical field of intelligent transportation, and particularly to autonomous driving technology. The traffic marker detection method comprises acquiring a target image containing a traffic marker; and inputting the target image into a traffic marker detection model to obtain a detection mark corresponding to the traffic marker; wherein the detection mark comprises at least one of a detection point and a detection line for characterizing a position of the traffic marker in the target image.