Abstract: A sensor system is disclosed. The sensor system may comprise a housing; an emitter, carried by the housing, that emits a beam comprising depth-data signals; a beam-distribution adjustment system; and a processor programmed to control the adjustment system by selectively changing an angular distribution of the depth-data signals emitted from the housing.

Abstract: A map of an area is generated from first sensor data of a first field of view including a blocked area and second sensor data of a second field of view having an unblocked view of the blocked area.

Abstract: A situational awareness system is described that comprises a processor and memory storing instructions executable by the processor. The instructions comprise to: determine at least one object of interest relative to a target vehicle; determine an assignment optimization at least one of a plurality of mobile devices to the at least one object of interest; and assign the at least one of the plurality of mobile devices to the at least one object of interest.

Abstract: A system comprises a computer including a processor, and a memory. The memory stores instructions such that the processor is programmed to determine two or more clusters of vehicle operating parameter values from each of a plurality of vehicles at a location within a time. Determining the two or more clusters includes clustering data from the plurality of vehicles based on proximity to two or more respective means. The processor is further programmed to determine a reportable condition when a mean for a cluster representing a greatest number of vehicles varies from a baseline by more than a threshold.

Abstract: A system includes a Lidar sensor and a processor. The processor is programmed to detect, using Lidar sensor data, a low-density area comprising a plurality of Lidar beam reflections less than a threshold, to determine dimensions of the area, and to determine that the area represents a physical object based on the detection and determination.

Abstract: A computer includes a processor and a memory. The memory stores instructions executable by the processor to receive, in a vehicle, object data from an external node, and upon identifying a point, in the received object data, that is within a volume defined based on vehicle position data received from a vehicle sensor, to determine an adjusted vehicle position based on the identified point and the vehicle position data.

Abstract: A reference pose graph can be generated from first point cloud data collected at each of a plurality of mobile nodes. A location of a stationary node can be specified based on the reference pose graph and second point cloud data collected by the stationary node.

Abstract: A computing system can receive, in a vehicle, moving object information is determined by processing lidar sensor data acquired by a stationary lidar sensor. The moving object information can be determined using typicality and eccentricity data analysis (TEDA) on the lidar sensor data. The vehicle can be operated based on the moving object information.

Abstract: A computing system receive first sensor data including moving objects and multi-modal LiDAR map data wherein the first sensor is calibrated based on a cost function that alternates between LiDAR map point cloud data and LiDAR map ground reflectivity data as input. The computing system can operate the vehicle based on the moving objects.

Abstract: A map of an area is generated from first sensor data of a first field of view including a blocked area and second sensor data of a second field of view having an unblocked view of the blocked area.

Abstract: A system and methods are described for calibrating one sensor with respect to another. A method includes: determining a depth-motion vector using a first sensor; determining an optical-motion vector using a second sensor; and calibrating the first sensor with respect to the second sensor by minimizing a cost function that evaluates a distance between the depth-motion and optical-motion vectors.