A system for inferring lane boundaries via vehicle telemetry for a road section is provided. The system includes a computerized remote server device operable to receive through a communications network sensor data describing lane markings upon roads bordering the road section, determine established lanes upon roads bordering the road section based upon the sensor data, receive through the communications network the vehicle telemetry generated by a plurality of vehicles traversing the road section, generate inferred lanes for the road section based upon the vehicle telemetry, match the inferred lanes to the established lanes to generate unified lane geometries, and publish the unified lane geometries.

The disclosure provides a method and apparatus for constructing a navigation element in a map, a computer device and a readable storage medium. Various kinds of road information are recognized through semantic map data, roads are divided through lane lines, and road elements are added to the divided road blocks according to location information of the road elements so as to obtain a navigation map.

This application relates to a lane edge extraction method and apparatus, an autonomous driving system, a vehicle, and a storage medium. The lane edge extraction method includes the steps of: receiving tracking edge points, about lane edges, of an immediately preceding frame of an edge image sequence; determining observation edge points, about the lane edges, of a current frame of the edge image sequence; continuing and correcting the tracking edge points of the immediately preceding frame based on the observation edge points of the current frame, to obtain temporary tracking edge points of the current frame; fitting a lane edge curve based on the temporary tracking edge points; and excluding outliers from the temporary tracking edge points based on the lane edge curve, to form tracking edge points of the current frame. The method can improve the stability and accuracy of lane edge extraction.

The disclosure relates to a method of predicting one or more road attributes. The method may include providing trajectory data of a geographical area. The method may further include providing map data, wherein the map data may include image data of the geographical area. The method may further include extracting trajectory features from the trajectory data and extracting map features from the map data. The method may further include using at least one processor to predict road attributes by inputting the trajectory features and the map features in a neural network and by classifying an output of the neural network into prediction probabilities of the road attributes. The disclosure also relates to a data processing system; to a non-transitory computer-readable medium storing computer executable code; and to a method of training an automated predictor.

A method for detecting a road change is provided. The method includes: obtaining a road image comprising a road to be detected; extracting a road region in the road image as a target region, wherein the road region corresponds to where the road to be detected is located; obtaining a target geographical location of the target region; determining a reference region for the target region from pre-stored road regions based on the target geographical location; calculating a similarity between the target region and the reference region; and determining, based on the similarity, whether a passability of the road to be detected is changed. By applying the solution provided by the embodiments of the present disclosure, efficiency of road change detection can be improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that create lane graph geometries from lane graph topologies. One of the methods includes receiving data representing a topological lane path through a plurality of cells of a drivable region. From the topological lane path, an initial polyline that traverses the same plurality of cells as the topological lane path can be generated. The initial polyline can be defined by vertices located on edges of a triangulated decomposition of the drivable region. A geometry optimization process can be performed on the initial polyline to generate a final polyline according to optimization criteria. From the final polyline, a geometric lane path representing a geometry of a drivable lane that traverses the drivable region can be generated.

A system that determines a nominal path based on interpolated lane edge data can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived lane edge; receive map data including a lane edge; interpolate the sensor data and interpolate the map data; fuse the interpolated sensor data and the interpolated map data; and generate predicted lane edge lane edge centers based on the fused interpolated sensor data.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that create lane graph topologies. One of the methods includes receiving data representing a drivable region of space that includes road obstacles. The representation of the drivable region can include data representing cells that represent interconnected drivable regions between the road obstacles. Each cell can contain edges, and each edge can represent that a drivable region exists between two cells. A request to generate a predicted lane graph topology for the drivable region can be received. A plurality of lane graph topologies can be enumerated, and each lane graph topology can include paths through the drivable region. A score can be computed for each lane graph topology. In response to the request, and based on the computed scores, at least one particular enumerated lane graph topology can be provided.

The disclosure relates to a road model generation method, the method including: receiving lane line information from a plurality of sources; determining a reference lane line; and performing arbitration and then selecting, from the lane line information from the plurality of sources, a first lane line corresponding to the reference lane line, where the first lane line and the reference lane line together form left and right lane lines of a present lane. The disclosure further relates to a road model generation device, a computer storage medium, an autonomous driving system, and a vehicle.

A vehicle driving control apparatus for controlling driving of a vehicle based on information received from a plurality of sensors, and a calibration method performed thereby is provided. The vehicle driving control apparatus includes at least one processor configured to determine a driving route to include a predetermined area of road, upon determining that calibration of a first sensor is required, and a memory configured to store values measured by the plurality of sensors while the vehicle is being driven along the driving route, wherein the at least one processor is further configured to calibrate the first sensor based on the stored measured values and information about the predetermined area of road.