Geodigital International Inc.

Ushr Inc.

Other

Pci Geomatics Enterprises Inc.

Richard James Pollock

Alastair Nigel Jenkins

Douglas Jared Parent

Brendan Betke

David Erik Stanley

Robert Moses

Louis Burry

A self-driving road vehicle stores an active driving map that includes a data store of route feature data together with a software or control system that periodically selects from the data store and provides to systems on the vehicle the route feature data that are relevant to the vehicle location and the route that the vehicle is following. The route feature data may include a sequential series of road cross-section data objects that represent a real-world roadway being traversed by the vehicle. Methods for operating the self-driving road vehicle include providing route feature data from an active driving map, which may include the sequential series of road cross-section data objects that represent the real-world roadway being traversed.

Active driving map for self-driving road vehicle

The present disclosure relates to an autonomous vehicle and methods of operating an autonomous vehicle. A sensor for the autonomous vehicle generates sensor data for a current location of the autonomous vehicle. A positioning system generates data for identifying the current location of the vehicle. A computing system of the autonomous vehicle identifies a portion of a roadway model which corresponds to the current location of the vehicle. The roadway model has a level of detail suitable for autonomous operation. The computing system identifies whether a discrepancy is present between the identified portion of the roadway model and the sensor data. When the computing system identifies the discrepancy, the computing system communicates data which corresponds to the discrepancy to a roadway model management system configured to update the roadway model.

Roadway model for a self-driving road vehicle

An attributed roadway trajectory comprises at least one ordered series of attributed roadway trajectory points, which are spaced along a curve that is defined in a terrestrial coordinate frame and tracks an along-roadway physical feature of a real-world roadway segment portrayed in a point cloud. Any arbitrary attributed roadway trajectory point on the curve passes a predetermined proximity test for proximity to point cloud points discriminated as representing part of the along-roadway physical feature. Each attributed roadway trajectory point has at least one attribute value representing a characteristic of the real-world roadway segment at a position on the real-world roadway segment spatially associated with the attributed roadway trajectory point. A control system of a self-driving road vehicle can use the attribute value(s) to adjust control of the self-driving road vehicle based on sensor-independent roadway data (e.g. where the position of the attributed roadway trajectory point(s) remains outside of sensor range).

Attributed roadway trajectories for self-driving vehicles

A computer-implemented method for determining which portions of a roadway model used by self-driving road vehicles require updating uses discrepancy data derived from the sensors of a plurality of self-driving road vehicles. The discrepancy data may indicate discrepancies between the sensor data and the roadway model, or may indicate portions of the roadway where a self-driving road vehicle underperformed. The discrepancy data is aggregated, and the aggregated discrepancy data is used to identify, as the portions of the roadway model which require updating, those portions of the roadway model corresponding to portions of the roadway for which the aggregated discrepancy data exceeds a threshold.

Determining portions of a roadway model requiring updating

Image data obtained from an image sampling of a physical surface is integrated with position data obtained from a three-dimensional surface sampling of the same physical surface by combining data from the images with the measured surface points from the surface sampling to create additional 'implied' surface points between the measured surface points. Thus, the originally obtained point cloud of measured surface points is densified by adding the implied surface points. Moreover, the image data can be used to apply colors to both the implied data points and the measured data points, resulting in a colored three-dimensional representation of the physical surface that is of higher resolution than a representation obtained from only the measured surface points.

Densifying and colorizing point cloud representation of physical surface using image data

The present invention is related to the processing of data, and more particularly to a method of and system for processing large volumes of Earth observation imagery data. A system for processing a large volume of Earth observation imaging data is described, comprising a computer including a visual display and a user interface, a plurality of servers, an image database storing said Earth observation imaging data as a plurality of separate image data files, and a network for interconnecting the computer, plurality of servers and image database. The plurality of servers is operable to process the separate data files in a distributed manner, at least one of the plurality of servers is operable to process the separate data files in a multiprocessing environment and at least one of the plurality of servers is operable to collate the processed separate data files into a single imaging result.

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Aurora, Canada

Richard James Pollock