A host vehicle-based feature harvester is disclosed. In one implementation, the feature harvester includes memory and a processor configured to receive a plurality of images captured by a camera onboard the host vehicle, the plurality of images being representative of an environment of the host vehicle; analyze at least one image from the plurality of images to identify a representation of a lane mark; select at least one sample area of the representation of the lane mark, wherein the at least one sample area is associated with an image location of at least a portion of the representation of lane mark; determine a location identifier of the at least one sample area; determine a surface quality indicator associated with the at least one sample area; and cause transmission of the location identifier and the surface quality indicator to an entity remotely-located relative to the host vehicle.

A method and system for estimating road lane geometry includes a camera-estimated lane segment, for estimating lane geometry based on camera detection of road markings and a leading-vehicle-estimated lane segment, for estimating lane geometry based on traces of at least one leading vehicle. Estimated road geometry is obtained from a combination of the camera-estimated lane segment and the leading-vehicle-estimated lane segment.

The present disclosure provides a method and an apparatus for processing a lane line, and relates to the field of data processing and, in particular, to the fields of intelligent transportation, Internet of Vehicles and intelligent cockpit. A specific implementation scheme is: obtaining a lane edge line of a road and a lane dividing line of the road according to point cloud data and image information of the road; acquiring breakpoints of the lane edge line, and acquiring breakpoints of the lane dividing line; completing the lane edge line according to the breakpoints of the lane edge line, to obtain a continuous lane edge line; completing the lane dividing line according to the breakpoints of the lane dividing line and the continuous lane edge line, to obtain a continuous lane dividing line.

In a method for reconstruction of a feature in an environmental scene of a road, a 3D point cloud of the scene and a sequence of 2D images of the scene are generated. A portion of candidates of 3D points of the 3D point cloud is identified by projecting the 3D points to each of the 2D images, determining a plurality of candidates of the 3D points of the 3D point cloud representing the feature by semantic segmentation in each of the images, projecting the candidates of the 3D points on a plane of the road in each of the 2D images, and selecting those candidates of the 3D points staying in a projection range on the road in each of the 2D images. The selected candidates of the 3D points are merged for determining estimated locations of the feature. The feature can be modeled by generating a fitting curve along the estimated locations.

The disclosure relates to a lane line determination method and system, a vehicle, and a storage medium, and the lane line determination method includes: capturing a road image for a current location in a vehicle coordinate system; recognizing the road image and generating basic lane lines for the current location; extracting map lane lines for the current location; mapping the map lane lines to the vehicle coordinate system to obtain auxiliary lane lines; registering the basic lane lines with the auxiliary lane lines, the registration being performed based on confidence levels of the basic lane lines; and generating target lane lines based on the registered auxiliary lane lines and the basic lane lines. According to the lane line determination method, an operation such as correction can be performed, according to a condition, on a visually captured lane line by using a map lane line, thereby improving accuracy of the lane line detection.

A display control device displays, on an electronic inner mirror, a composite image composited by performing image processing on a rear image captured by a rear camera and rear lateral images captured by an outer camera unit such that the rear image and the rear lateral images become a continuous image. A subject vehicle virtual image superimposition display unit of the display control device generates a virtual image of a subject vehicle virtually showing a subject vehicle and displays the virtual image of the subject vehicle superimposed on the composite image. A subject vehicle travel trajectory superimposition display unit of the display control device generates a virtual image of travel trajectory, which extends from left and right rear wheels toward the rear of the vehicle in the composite image, and displays the virtual image of travel trajectory superimposed on the composite image.

A method for locating an object of interest using offset. The object may be a mobile platform, or portion of same, associated with a vehicle, or a pavement segment or feature of or on a pavement segment on which the mobile platform is located. The vehicle includes first and second fixed points having a known offset from each other. An image sensor whose field of view includes the second fixed point and a portion of the mobile platform provides image data which is used with the known offset to calculate the precise location of the object of interest.

A work vehicle includes a first detection unit that detects an optical beam emitted from a beam projector disposed at one end of a reference travel path, a first position deviation calculation section that calculates position deviation by a vehicle body from the reference travel path based on a detection signal from the first detection unit, a second detection unit that detects a work boundary line that occurs due to work travel, a second position deviation calculation section that calculates position deviation of the vehicle body traveling along successive travel paths from the work boundary line based on a detection signal from the second detection unit, and a steering information generation section that, based on the position deviation calculated by the first position deviation calculation section and the second position deviation calculation section, generates steering information for correcting the position deviation.

Automated training dataset generators that generate feature training datasets for use in real-world autonomous driving applications based on virtual environments are disclosed herein. The feature training datasets may be associated with training a machine learning model to control real-world autonomous vehicles. In some embodiments, an occupancy grid generator is used to generate an occupancy grid indicative of an environment of an autonomous vehicle from an imaging scene that depicts the environment. The occupancy grid is used to control the vehicle as the vehicle moves through the environment. In further embodiments, a sensor parameter optimizer may determine parameter settings for use by real-world sensors in autonomous driving applications. The sensor parameter optimizer may determine, based on operation of the autonomous vehicle, an optimal parameter setting of the parameter setting where the optimal parameter setting may be applied to a real-world sensor associated with real-world autonomous driving applications.

A map generation device (10) generates linearization information expressing at least one or the other of a marking line of a roadway and a road shoulder edge based on measurement information of a periphery of the roadway. The measurement information is obtained by a measurement device. The map generation device (10) calculates an evaluation value expressing a reliability degree of partial information, for each partial information constituting the linearization information. A map editing device (20) displays the partial information in different modes according to the evaluation value, thereby displaying the linearization information. The map editing device (20) accepts input of editing information for the displayed linearization information.