An information processing apparatus includes circuitry. The circuitry controls a display to display primary analysis information obtained by analyzing measurement information without being based on a request for an analysis. The measurement information is obtained by measuring an object. The circuitry accepts an analysis request that requests secondary analysis information of the measurement information corresponding to the primary analysis information.

Certain aspects of the present disclosure provide techniques for lane marker detection. A set of feature tensors is generated by processing an input image using a convolutional neural network. A set of localizations is generated by processing the set of feature tensors using a localization network, a set of horizontal positions is generated by processing the set of feature tensors using row-wise regression, and a set of end positions is generated by processing the set of feature tensors using y-end regression. A set of lane marker positions is determined based on the set of localizations, the set of horizontal positions, and the set of end positions.

Disclosed herein are a driver assistance system and a vehicle including the same. The driver assistance system of the present disclosure includes a camera, an obstacle detector configured to detect an obstacle and output obstacle information about the detected obstacle, and a processor configured to recognize an image of the obstacle based on image information acquired by the camera, obtain a rate of change in size of the recognized image of the obstacle, obtain relative bearing information of the obstacle based on the obstacle information detected by the obstacle detector, determine whether the obstacle and a vehicle are present on a collision path based on the obtained rate of change in size of the image of the obstacle and the relative bearing information of the obstacle, and upon determining that the vehicle and the obstacle are present on the collision path, output a collision risk warning.

An approach is provided for lane width estimation from incomplete lane marking detections of a road lane. The approach, for example, involves generating one or more perpendicular lines respectively from location centers of one or more first lane marking detections. A respective lane marking detection represents at least a portion of a boundary of the road lane as a line delimited by two location data points in accordance with detections by at least one sensor device onboard at least one vehicle. The approach also involves identifying second lane marking detections that each respectively intersect one of the one or more perpendicular lines. The approach further involves selecting one or more candidate lane widths based on one or more respective distances from the location centers to the second lane marking detections. The approach further involves determining an estimated lane width of the road lane based on the one or more candidate lane widths.

In various examples, image space coordinates of an image from a video may be labeled, projected to determine 3D vehicle space coordinates, then transformed to 3D world space coordinates using known 3D world space coordinates and relative positioning between the coordinate spaces. For example, 3D vehicle space coordinates may be temporally correlated with known 3D world space coordinates measured while capturing the video. The known 3D world space coordinates and known relative positioning between the coordinate spaces may be used to offset or otherwise define a transform for the 3D vehicle space coordinates to world space. Resultant 3D world space coordinates may be used for one or more labeled frames to generate ground truth data. For example, 3D world space coordinates for left and right lane lines from multiple frames may be used to define lane lines for any given frame.

A method and system for determining a geographical location and orientation of a vehicle (200, 300) travelling through a road network is disclosed. The method comprises obtaining, from one or more cameras (202, 302) associated with the vehicle (200, 300) travelling through the road network, a sequence of images (500) reflecting the environment of the road network on which the vehicle (200, 300) 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 (200, 300) 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 (200, 300) is travelling, and the geographical location and orientation of the vehicle (200, 300) 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 (200, 300) travelling through a road network represented by the map are also disclosed.

A method for displaying lane information for a vehicle, includes obtaining a digital map; defining localization information of the vehicle; obtaining a route plan of the vehicle; determining a driving lane according to the localization information of the vehicle and the route plan of the vehicle; obtaining coordinates information of lane markings of the driving lane from the digital map; receiving image data from a camera mounted on the vehicle; transforming the lane markings of the driving lane from a coordinate system of the digital map to a coordinate system of the camera; generating a lane guide sign indicating the lane markings of the driving lane in the coordinate system of the camera based on the lane markings of the driving lane transformed to the coordinate system of the camera; and superimposing the lane guide sign of the lane markings of the driving lane on the image data received from the camera.

A lane line detection method, an electronic device, and a storage medium, related to the field of artificial intelligence, and particularly related to computer vision and deep learning technologies, which can be applied to intelligent traffic scenes, are provided. The method includes: dividing an image into a foreground region and a background region; determining a solid line and a dotted line included in the foreground region; determining, according to the solid line and the dotted line comprised in the foreground region, whether a dotted-and-solid line is included in the foreground region; and determining a lane line detection result according to the solid line, the dotted line, and whether a dotted-and-solid line is comprised in the foreground region. According to the technical solution, the accuracy of lane line detection can be improved.

A method is provided that may include obtaining image data related to a route from a imaging device associated with a vehicle, determining a geometric pattern related to the route based on the image data, and identifying the geometric pattern determined within a route database to determine the route of the vehicle.

According to an embodiment, a drive control device configured to calculate lane attribute information including at least one of a lane recommendation degree of each lane included in lane information on a road to be traveled, a propriety of traveling each lane, and a target speed in each lane, based on own vehicle information and second vehicle information including position information and speed information on an own vehicle and a second vehicle present at a periphery of the own vehicle, route information, and map information; and determine at least one of a travel lane and a speed of the own vehicle within a range in which safety is guaranteed, using a machine learning model that receives the own vehicle information, the second vehicle information, the route information, the map information, and the lane attribute information, and outputs at least one of a travel lane and a speed.