Abstract: Aspects of the disclosed invention relate to alignment of standard definition (SD) maps and high definition (HD maps) which may come from different sources. Responsive to input of a destination, a route to that destination may be defined, and SD map waypoints generated from that defined route. A graph may be generated from the HD map. The waypoints may be matched with nodes and edges in the graph. One or more edges may constitute a segment in the HD map. A plurality of segments are identified to match the route.

Abstract: Aspects of the disclosed invention relate to alignment of standard definition (SD) maps and high definition (HD maps) which may come from different sources. Responsive to input of a destination, a route to that destination may be defined, and SD map waypoints generated from that defined route. A graph may be generated from the HD map. The waypoints may be matched with nodes and edges in the graph. One or more edges may constitute a segment in the HD map. A plurality of segments are identified to match the route.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.