An evaluation computing system may implement techniques to validate a vehicle controller, such as based on a detection of a systematic fault. The evaluation computing system may access data (e.g., log data and/or map data) associated with an operation of the vehicle in an environment as controlled by the controller. The evaluation computing system may modify a portion of the map data representative of a simulated change associated with a portion of the environment. The evaluation computing system may run a simulation with a simulated environment, generated based on the modified map data, to determine whether the controller detects and/or mitigates the simulated change in a sufficient manner. Based on a determination of whether or not the controller detects and/or mitigates the simulated change in a sufficient manner, the evaluation computing system may determine an error associated with the controller or may validate the controller.

A generation device includes processing circuitry configured to receive a plurality of inputs of road map data including longitude/latitude data on a road shoulder line and longitude/latitude data on a lane marker, and refer to the road map data, set a region surrounded by the road shoulder line to be a non-road region, and generate a first polygon indicating a lane region using data on two adjacent non-road regions and on a plurality of lane markers positioned between the two non-road regions.

An approach is provided for confirming road vector geometry based on aerial image(s). For example, the approach involves retrieving a feature and a vector representation of a road link. The approach also involves processing one or more aerial images depicting the road link to extract a list of spectral pixel values corresponding to the vector representation. The approach further involves determining a degree of misalignment between the spectral pixel values and a spectral signature of the feature of the road link. The approach further involves initiating a confirmation of a geometry of the vector representation based on the degree of misalignment. The approach further involves providing the confirmation as an output.

According to an aspect of an embodiment, operations may comprise obtaining a pose graph that comprises a plurality of nodes. The operations may also comprise dividing the pose graph into a plurality of pose subgraphs, each pose subgraph comprising one or more respective pose subgraph interior nodes and one or more respective pose subgraph boundary nodes. The operations may also comprise generating one or more boundary subgraphs based on the plurality of pose subgraphs, each of the one or more boundary subgraphs comprising one or more respective boundary subgraph boundary nodes and comprising one or more respective boundary subgraph interior nodes. The operations may also comprise obtaining an optimized pose graph by performing a pose graph optimization. The pose graph optimization may comprise performing a pose subgraph optimization of the plurality of pose subgraphs and performing a boundary subgraph optimization of the plurality of boundary subgraphs.

Techniques for generating and validating map data that may be used by a vehicle to traverse an environment are described herein. The techniques may include receiving sensor data representing an environment and receiving map data indicating a traffic control annotation. The traffic control annotation may be associated, as projected data, with the sensor data based at least in part on a position or orientation associated with a vehicle. Based at least in part on the association, the map data may be updated and sent to a fleet of vehicles. Additionally, based at least in part on the association the vehicle may determine to trust the sensor data more than the map data while traversing the environment.

A method for generating a map display for vehicles. The method includes receiving vehicle sensor data; ascertaining poses of the vehicles based on the vehicle sensor data; generating subsets of the plurality of poses by combining poses of drives that have been carried out on an identical subsection of the route, a course of the respective subsection of the route being described by the vehicle sensor data assigned to the poses of every subset; generating sub-segments of the map display based on the subsets of the poses, for each subset a sub-segment being generated, and a course of the traffic lane within a sub-segment corresponding to one of the subsets being ascertained based on the poses of a subset; interpreting the sub-segments as nodes of a graph display of the map display and connecting sub-segments via edges of the graph display.

A method for constructing a lane level map using a three-dimensional point cloud map is provided.

According to the method, during scan matching for estimating the location of a vehicle in the process of automatically constructing a 3D high-definition map, the amount of computation is reduced by reducing the size of a target 3D map. Thereby the method is performed fast and accurate position estimation. In addition, even if the position estimation by scan matching fails, more robust position estimation is possible by estimating the location of the vehicle using LiDAR odometry performed in parallel. The method builds and merges a precise lane map with a pre-built 3D point cloud map using such robust localization to build a more precise 3D precise map and a lane node-link map. By using these three-dimensional precise maps and maps that generate node-links in lanes, a more effective route planning algorithm that can be provided.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

The present disclosure is related to generating map data explicitly indicating a total drivable surface, which may include multiple types of drivable surfaces. For instance, a given portion of a map may include map data indicating a combination of various drivable surfaces, such as road segments, lane properties, intersections, parking areas, shoulders, driveways, etc. Examples of the present disclosure join these different types of drivable surfaces into combined map data that explicitly indicates a total drivable surface, such as a perimeter boundary indicating or representing a transition from a drivable surface to a non-drivable surface. The map data indicating the total drivable surface may be searched to determine information related to a drivable surface boundary, such as location and type. This boundary information may be used in various contexts, such as when planning a trajectory or remotely controlling a vehicle.

A method and apparatus are provided for determining whether a driving environment has changed relative to previously stored information about the driving environment. The apparatus may include an autonomous driving computer system configured to detect one or more vehicles in the driving environment, and determine corresponding trajectories for those detected vehicles. The autonomous driving computer system may then compare the determined trajectories to an expected trajectory of a hypothetical vehicle in the driving environment. Based on the comparison, the autonomous driving computer system may determine whether the driving environment has changed and/or a probability that the driving environment has changed, relative to the previously stored information about the driving environment.