Disclosed is a surround view monitoring system comprising: a plurality of cameras provided in a vehicle, the cameras being configured to capture first images of surroundings of the vehicle; a non-volatile memory configured to store second images captured by the cameras or position information of the cameras in the vehicle; and a processor configured to match the first images captured by the cameras relative to one another based on the second images or the position information stored in the non-volatile memory.

A vehicle localization method and a vehicle localization apparatus are disclosed. The vehicle localization method includes estimating an initial position of a vehicle based on data sensed by a position sensor, determining a driving lane of the vehicle based on a front-view image captured from the vehicle, correcting the initial position of the vehicle based on the driving lane, and determining a position of the vehicle in the driving lane based on geometry information of a lane boundary in the front-view image.

In a system for specifying a lane marking deterioration, an image acquiring unit acquires a captured image of an outside of a vehicle. A specifying unit extracts a lane marking on a road surface included in the captured image and specifies a deteriorated portion of the lane marking. The specifying unit specifies the deteriorated portion of the lane marking in comparison with another lane marking included in the captured image. A receiving unit receives past lane marking data of a road included in the captured image. The specifying unit specifies, from the captured image, a deteriorated portion of the lane marking that has deteriorated from the past lane marking data.

In one embodiment, a method includes receiving sensor data corresponding to an environment external of a vehicle. The sensor data include data points. The method includes determining one or more subsets of the data points. The method includes comparing the one or more subsets of the data points to one or more predetermined data patterns. Each of the one or more predetermined data patterns corresponds to an object classification. The method includes computing a confidence score for each subset of data points of the one or more subsets of the data points as corresponding to each of the one or more predetermined data patterns based on the comparison. The method includes generating a classification for an object in the environment external of the vehicle based on the confidence score.

The present invention relates to a lighting system 200 of an automotive vehicle comprising:—an image capture device (205) configured to acquire an image (I) of a road (R) of travel of the vehicle, said road (R) comprising lanes marked on the road (110);—a lighting module (215) configured to project road markings on the road (120); wherein said lighting system (200) is configured to filter the projected road markings (120) on the road compared to the lanes marked (110) on the road.

Systems and methods for navigating a host vehicle are disclosed. In one implementation at least one processor is programmed to receive at least one image captured by a camera from an environment of the host vehicle; analyze the at least one image to identity a representation of a lane of travel of the host vehicle along a road segment and a representation of at least one additional lane of travel along the road segment; analyze the at least one image to identify an attribute associated with the at least one additional lane of travel; determine, based on the attribute, information indicative of a characterization of the at least one additional lane of travel; and send the information indicative of the characterization of the at least one additional lane of travel to a server for use in updating a road navigation model.

Determining three-dimensional structure in a road environment using a system mountable in a host vehicle including a camera connectible to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, a line is selected below which the road is imaged. The line separates between upper images essentially excluding images of the road and lower images essentially including images of the road. One or more of the lower images is warped, according to a road homography to produce at least one warped lower image. The three-dimensional structure may be provided from motion of a matching feature within the upper images or from motion of a matching feature within at least one of the lower images and at least one warped lower image.

An electronic device is disclosed. The disclosed electronic device comprises: a camera module; one or more processors electrically connected to the camera module; and a memory electrically connected to the processors, wherein, when the electronic device operates, the memory can store instructions for making the processors generate an image by using the camera module, calculate angles formed by a virtual horizontal line and a plurality of straight lines included in the image, and select two straight lines on the basis of the calculated angles. Additional various examples are possible.

A vehicular driving assist system includes a camera disposed at a vehicle equipped with the vehicular driving assist system and viewing forward of the vehicle, the camera capturing image data. An electronic control unit (ECU) includes electronic circuitry and associated software. The electronic circuitry of the ECU includes an image processor for processing image data captured by the camera. The ECU, responsive to processing by the image processor of image data captured by the camera, determines presence of a leading vehicle traveling in front of the equipped vehicle and in the same traffic lane as the equipped vehicle. The ECU, responsive to determining presence of the leading vehicle, determines presence of a pothole in front of the vehicle and in the same traffic lane as the equipped vehicle.

A method of retrieving a map is disclosed. The method includes receiving a grid data of the map comprising lane segments, wherein the grid data includes an array of grids each associated with a list including none or at least one of the lane segments intersecting the respective grid; receiving coordinates of a location; identifying a first grid including the location based on the grid data; identifying a target grid that has an associated list including at least one of the lane segments as first lane segment; and outputting the first lane segment.