Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.

Abstract: Techniques for segmenting and classifying a representation of aggregated sensor data from a scene are discussed herein. Sensor data may be collected during multiple traversals of a same scene, and the sensor data may be filtered to remove portions of the sensor data not relevant to road network maps. In some examples, the filtered data may be aggregated and represented in voxels of a three-dimensional voxel space, from which an image representing a top-down view of the scene may be generated, though other views are also contemplated. Operations may include segmenting and/or classifying the image e.g., by a trained machine-learned model, to associate class labels indicative of map elements (e.g., driving lane, stop line, turn lane, and the like) with segments identified in the image. Additionally, techniques may create or update road network maps based on segmented and semantically labeled image(s) of various portions of an environment.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.

Abstract: Techniques are disclosed for tracking objects in sensor data, such as multiple images or multiple LIDAR clouds. The techniques may include comparing segmentations of sensor data such as by, for example, determining a similarity of a first segmentation of first sensor data and a second segmentation of second sensor data. Comparing the similarity may comprise determining a first embedding associated with the first segmentation and a second embedding associated with the second segmentation and determining a distance between the first embedding and the second embedding. The techniques may improve the accuracy and/or safety of systems integrating the techniques discussed herein.

Abstract: Techniques are disclosed for tracking objects in sensor data, such as multiple images or multiple LIDAR clouds. The techniques may include comparing segmentations of sensor data such as by, for example, determining a similarity of a first segmentation of first sensor data and a second segmentation of second sensor data. Comparing the similarity may comprise determining a first embedding associated with the first segmentation and a second embedding associated with the second segmentation and determining a distance between the first embedding and the second embedding. The techniques may improve the accuracy and/or safety of systems integrating the techniques discussed herein.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.

Abstract: Techniques are described for detecting whether a lane of a roadway is open or closed. Detecting a lane as being closed may include detecting an object in or near the lane, which may comprise determining a size, location, and/or classification associated with the object, and dilating the size associated with the object. The lane may be indicated as being closed if a distance between a dilated object detection and another object detection, dilated object detection, or lane extent is less than a threshold distance. The techniques may additionally or alternatively comprise determining an alternative lane shape based at least in part on one or more object detections and/or determining that one or more lanes are closed and/or uploading a lane closure and/or alternative lane shape to a central database for retrieval by/dissemination to other computing devices.