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.

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.

The present application discloses a vehicle position determining method, an apparatus, an electronic device, and relates to the technical fields of artificial intelligence, automatic driving, intelligent transportation and computer vision. A specific implementation scheme is: acquiring a current position of a vehicle to be positioned and characteristic information of a road ahead of the current position when determining the road where the vehicle is located in; and determining at least one road in a preset area including the current position in a navigation map; determining a probability that the vehicle to be positioned is located in each lane of each road according to the characteristic information of the road ahead; and determining a target road where the vehicle to be positioned is located in according to the probability of each lane.

System and methods are provided for a supervised point map matcher. The supervised point map matcher learns parameters from historical data that provide insight into the optimal probabilistic metrics that inform the bias of probes heading and distance for segments on the roadway. Probability weights for segments are generated. A more accurate path based map matching algorithm is used to identify direction and heading errors in the historical probe data. Values for the probability weights are calculated using kernel density estimation and a gaussian probability density function. The probability weights are used to improve the real time performance of the point map matcher. A confidence value is calculated as a function of the probability weights and provided with the map matched results.

Systems and methods are provided for controlling vehicle operation. A processor may access route information for navigation of a route by the vehicle including data relating to speed along the route and calculate a speed of the vehicle along the route based on the route information. The processor may cause the vehicle to be operated at the calculated speed along the route; obtain dynamic information for the route based on data collected from one or more other vehicles on the route and indicating current conditions on the route which affect the speed of the vehicle along the route; and cause the vehicle to be operated at an updated speed along the route, based on the dynamic information.

The present invention relates to a feature data generation system that is capable of securing accuracy of a feature database. In a feature data generation system 100, an edge pattern indicating a boundary of a target feature existing around a vehicle 200 is extracted from position data of a large number of measurement points surrounding the vehicle 200 measured using a LIDAR technology, on the basis of image data of surroundings of the vehicle 200 obtained by imaging so as to reduce the amount of information, positions in a terrestrial reference frame are assigned to the extracted edge pattern, and a shape characteristic vector and a feature characteristic vector to be used for generating or updating a feature in a feature database, are generated from the edge pattern to which the positions in the terrestrial reference frame are assigned.

Systems and methods are disclosed for identifying landmarks. A method for identifying a landmark may include initiating identification of a landmark based on one or more images from a camera, for use in autonomous vehicle navigation, the landmark including a traffic sign; initiating updating a road model with a location of the landmark; and initiating distribution of the road model with the location of the traffic sign to a plurality of autonomous vehicles.

A computing device configured to: obtain images representative of an environment of a host vehicle, the host vehicle traveling on a roadway; detect, from the images, a mark located on the roadway; identify, from the images, points corresponding to the mark on the roadway; identify the mark as a type of roadway marking, corresponding to the identified points, the type of roadway marking selected from multiple types of roadway markings; determine a position of the mark on the roadway relative to the host vehicle, using the identified points corresponding to the mark; and determine a trajectory to navigate the host vehicle on the roadway, based on the position of the mark within the roadway and the type of roadway marking.

Among other things, we describe techniques for automatic annotation of environmental features in a map during navigation of a vehicle. The techniques include receiving, by the vehicle located within an environment, a map of the environment. Sensors of the vehicle receive sensor data and semantic data. The sensor data includes a plurality of features of the environment. A geometric model of a feature of the plurality of features is generated. The feature is associated with a drivable area within the environment. A drivable segment is extracted from the drivable area. The drivable segment is segregated into a plurality of geometric blocks, wherein each geometric block corresponds to a characteristic of the drivable area and the geometric model of the feature includes the plurality of geometric blocks. The geometric model is annotated using the semantic data. The annotated geometric model is embedded within the map.