A map information correction method for correcting map information includes information of a lane boundary line, the method further including: detecting a position with respect to an own vehicle of a lane boundary line set in place on a road surface around the own vehicle; estimating an own position on a map of the own vehicle; and correcting, depending on the estimated own position and the detected position of the lane boundary line, a position of the lane boundary line included in the map information by, in a first region comparatively close to the own vehicle, a larger rotational correction amount than in a second region comparatively far from the own vehicle and, in the second region, a larger translational correction amount than in the first region.

A method, an apparatus and a computer program for real-time generation of functional road maps. The method comprises obtaining a real-time input from a sensor mounted on a vehicle, that captures a front view of a road ahead of the vehicle and processing thereof by a neural network to generate a functional map of the road ahead of the vehicle. Each pixel in the functional map is associated with a predetermined relative position to the vehicle. A content of each pixel is assigned a set of values, each of which represents a functional feature relating to a location at a corresponding predetermined relative position to the pixel. The processing is performed without relying on a pre-determined precise mapping. The method further comprises providing the functional map to an autonomous navigation system of the vehicle, to autonomously drive the vehicle in accordance with functional features represented by the functional map.

The present disclosure relates generally to identification of drivable surfaces in connection with autonomously performing various tasks at industrial work sites and, more particularly, to techniques for distinguishing drivable surfaces from non-drivable surfaces based on sensor data. A framework for the identification of drivable surfaces is provided for an autonomous machine to facilitate it to autonomously detect the presence of a drivable surface and to estimate, based on sensor data, attributes of the drivable surface such as road condition, road curvature, degree of inclination or declination, and the like. In certain embodiments, at least one camera image is processed to extract a set features from which surfaces and objects in a physical environment are identified, and to generate additional images for further processing. The additional images are combined with a 3D representation, derived from LIDAR or radar data, to generate an output representation indicating a drivable surface.

In one aspect, a vehicle localization system implements the following steps: receiving a predetermined road map; receiving at least one captured image from an image capture device of a vehicle; processing, by a road detection component, the at least one captured image, to identify therein road structure for matching with corresponding structure of the predetermined road map, and determine a location of the vehicle relative to the identified road structure; and using the determined location of the vehicle relative to the identified road structure to determine a location of the vehicle on the road map, by matching the road structure identified in the at least one captured image with the corresponding road structure of the predetermined road map.

A method and system for constructing a transverse topological relationship and a memory are provided. In the method, lane group data in a high-definition map is acquired; for each lane group, a shared boundary line group of two adjacent lanes is sequentially extracted, and the number of parallel boundary line elements is determined; if the number is 1, a transverse topological relationship between the two adjacent lanes is not generated, otherwise the number and types of boundary line units on the parallel boundary line elements are determined; and if the number of the boundary line units is 1, the transverse topological relationship between the two adjacent lanes is generated, otherwise segmentation processing is performed on the lane group along a lane direction, and the transverse topological relationship between two adjacent lanes in each of segments, which are obtained by the segmentation processing on the lane group, is sequentially generated.

Systems and methods may selectively collect information from a host vehicle. In one example, ft method may include causing collection of navigational information associated with an environment traversed by the host vehicle; storing the collected navigational information: determining, based on an output of at least one navigational sensor, a location of the host vehicle; transmitting the determined location of the host vehicle to a server: receiving, from the server and in response to the transmitted determined location, a request for transmission of a selected subset of the navigational information. collected by the host vehicle; and transmitting the selected subset of the navigational information to the server.

This application discloses a road data processing method, an apparatus, a device and a storage medium, and relates to the technical fields of intelligent traffic in data processing, big data and cloud computing. The specific implementation solution is: obtaining shape information of a target road and shape information of a reference road; generating a first segment sequence according to the shape information of the target road; generating a second segment sequence according to the shape information of the reference road; and recognizing parallel road segments in the target road and the reference road according to the first segment sequence and the second segment sequence. Thus, by converting the target road and the reference road into sequences of segments respectively, parallel road segments are recognized in the target road and the reference road based on the sequences of segments, the efficiency and accuracy are improved in recognizing parallel roads.

A system for generating map information for one or more road sections of a digital road map comprises an interface for receiving data records for the one or more road sections. The data records describe properties of surfaces outside the immediate road area. The system further comprises a first module for evaluating the received data records in order to identify first surfaces outside the immediate road area that are able to be driven on by a vehicle after leaving the road and on which the vehicle can be brought to a standstill after leaving the road, a second module for generating a description of the first surfaces in a format suitable for digital road maps, and a third module for retrievably providing the description of the first surfaces in one or more formats suitable for digital road maps.

Disclosed are systems and methods relating to providing corridor metrics based on road network data and telematic data.

In one embodiment, a system identifies a road to be navigated by an ADV, the road being captured by one or more point clouds from one or more LIDAR sensors. The system extracts road marking information of the identified road from the point clouds, the road marking information describing one or more road markings of the identified road. The system partitions the road into one or more road partitions based on the road markings. The system generates a point cloud map based on the road partitions, where the point cloud map is utilized to perceive a driving environment surrounding the ADV.