Abstract: One or more embodiments of the present disclosure relate to generation of map data. In these or other embodiments, the generation of the map data may include determining whether objects indicated by the sensor data are static objects or dynamic objects. Additionally or alternatively, sensor data may be removed or included in the map data based on determinations as to whether it corresponds to static objects or dynamic objects.

Abstract: Embodiments of the present disclosure relate to a system and method used to localize one or more systems using 2D map data. The method may include determining an image location of a representation of a portion of an object in an image corresponding to an environment. In some embodiments, the method may additionally include determining one or more predicted image locations corresponding to the image location of the representation of the portion of the object. The method may additionally include comparing one or more ground plane locations of the portion of the object with the one or more predicted image locations, and determining a cost based at least on the comparison between the one or more ground plane locations and the one or more predicted image locations. Further, the method may include localizing a system to the 2D map data based on the determined cost.

Abstract: In various examples, accuracy determinations for localization in autonomous and semi-autonomous systems and applications are described herein. Systems and methods are disclosed that determine one or more errors associated with vehicle localization using various types of sensor data generated using a vehicle. For instance, a first component of the vehicle may use a map and first sensor data to determine an estimated pose of the vehicle. A second component of the vehicle may then determine the error(s) associated with the estimated pose based on both actual motion of the vehicle within the environment, as determined using second sensor data, and comparing features represented by the first sensor data to features represented by the map. In some examples, the second component may further determine information associated with the error(s), such as one or more uncertainties associated with the error(s).

Abstract: One or more embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be communicated to a distributed map system that is configured to generate map data based on the RADAR point clouds. In some embodiments of the present disclosure, certain compression operations may be performed on the RADAR point clouds to reduce the amount of data that is communicated from the ego-machines to the map system.

Abstract: Systems and methods for vehicle-based determination of HD map update information. Sensor-equipped vehicles may determine locations of various detected objects relative to the vehicles. Vehicles may also determine the location of reference objects relative to the vehicles, where the location of the reference objects in an absolute coordinate system is also known. The absolute coordinates of various detected objects may then be determined from the absolute position of the reference objects and the locations of other objects relative to the reference objects. Newly-determined absolute locations of detected objects may then be transmitted to HD map services for updating.

Abstract: In various examples, perception of landmark shapes may be used for localization in autonomous systems and applications. In some embodiments, a deep neural network (DNN) is used to generate (e.g., per-point) classifications of measured 3D points (e.g., classified LiDAR points), and a representation of the shape of one or more detected landmarks is regressed from the classifications. For each of one or more classes, the classification data may be thresholded to generate a binary mask and/or dilated to generate a densified representation, and the resulting (e.g., dilated, binary) mask may be clustered into connected components that are iteratively: fitted a shape (e.g., a polynomial or Bezier spline for lane lines, a circle for top-down representations of poles or traffic lights), weighted, and merged. As such, the resulting connected components and their fitted shapes may be used to represent detected landmarks and used for localization, navigation, and/or other uses.

Abstract: In various examples, one or more DNNs may be used to detect landmarks (e.g., lane lines) and regress a representation of their shape. A DNN may be used to jointly generate classifications of measured 3D points using one output head (e.g., a classification head) and regress a representation of one or more fitted shapes (e.g., polylines, circles) using a second output head (e.g., a regression head). In some embodiments, multiple DNNs (e.g., a chain of multiple DNNs or multiple stages of a DNN) are used to sequentially generate classifications of measured 3D points and a regressed representation of the shape of one or more detected landmarks. As such, classified landmarks and corresponding fitted shapes may be decoded and used for localization, navigation, and/or other uses.

Abstract: Systems and methods for vehicle-based determination of HD map update information. Sensor-equipped vehicles may determine locations of various detected objects relative to the vehicles. Vehicles may also determine the location of reference objects relative to the vehicles, where the location of the reference objects in an absolute coordinate system is also known. The absolute coordinates of various detected objects may then be determined from the absolute position of the reference objects and the locations of other objects relative to the reference objects. Newly-determined absolute locations of detected objects may then be transmitted to HD map services for updating.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: In various examples, a lane planner for generating lane planner output data based on a state and probabilistic action space is provided. A driving system—that operates based on a hierarchical drive planning framework—includes the lane planner and other planning and control components. The lane planner processes lane planner input data (e.g., large lane graph, source node, target node) to generate lane planner output data (e.g., expected time rewards). The driving system can also include a route planner (e.g., a first planning layer) that operates to provide the lane planner input data to the lane planner. The lane planner operates as second planning layer that processes the lane planner input data based at least in part on a state and probabilistic action space of the large lane graph and calculates a time cost associated with navigating from a source node to a target node in the large lane graph.

Abstract: Embodiments of the present disclosure relate to a method of translating routes between maps. The method may include obtaining a graph based on data of an area. The graph may include one or more nodes representing different locations along one or more navigable paths as defined by the map. The method may also include obtaining one or more waypoints that define a route to traverse in the area and selecting, from the nodes, one or more path nodes based on locations of the path nodes corresponding to locations of the way points. The selected path nodes may define a path in the data that corresponds to the route.

Abstract: One or more embodiments of the present disclosure relate to generation of map data. In these or other embodiments, the generation of the map data may include determining whether objects indicated by the sensor data are static objects or dynamic objects. Additionally or alternatively, sensor data may be removed or included in the map data based on determinations as to whether it corresponds to static objects or dynamic objects.

Abstract: One or more embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be communicated to a distributed map system that is configured to generate map data based on the RADAR point clouds. In some embodiments of the present disclosure, certain compression operations may be performed on the RADAR point clouds to reduce the amount of data that is communicated from the ego-machines to the map system.

Abstract: Embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be used to generate map data. Additionally or alternatively, the RADAR point clouds may be used for performing localization.

Abstract: One or more embodiments of the present disclosure relate to aligning sensor data. In some embodiments, the aligning may be used for performing localization. In these or other embodiments, the aligning may be used for map creation.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams – or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data – corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data – and ultimately a fused high definition (HD) map – that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Abstract: In various examples, health of a high definition (HD) map may be monitored to determine whether inaccuracies exist in one or more layers of the HD map. For example, as one or more vehicles rely on the HD map to traverse portions of an environment, disagreements between perception of the one or more vehicles, map layers of the HD map, and/or other disagreement types may be identified and aggregated. Where errors are identified that indicate a drop in health of the HD map, updated data may be crowdsourced from one or more vehicles corresponding to a location of disagreement within the HD map, and the updated data may be used to update, verify, and validate the HD map.

Abstract: Systems and methods for vehicle-based determination of HD map update information. Sensor-equipped vehicles may determine locations of various detected objects relative to the vehicles. Vehicles may also determine the location of reference objects relative to the vehicles, where the location of the reference objects in an absolute coordinate system is also known. The absolute coordinates of various detected objects may then be determined from the absolute position of the reference objects and the locations of other objects relative to the reference objects. Newly-determined absolute locations of detected objects may then be transmitted to HD map services for updating.