Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive operation data generated by first components of a vehicle; determine that the vehicle is being operated autonomously by an on-board autonomous-operation module based on the operation data without using output from the on-board autonomous-operation module; and upon determining that the vehicle is being operated autonomously, actuate a second component of the vehicle.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to predict a quantity of misalignment of an optical sensor based on a projected motion of a vehicle, predict an error of the predicted quantity of misalignment, and actuate the vehicle based on the predicted quantity of misalignment and the predicted error. The vehicle includes the optical sensor.

Abstract: Rider identification systems and methods using motion sensing are disclosed herein. An example method includes obtaining a location of a mobile device associated with an individual, obtaining sensor data from the mobile device that includes a first motion profile, the sensor data also including environment information around the location, generating a motion model for the individual using the environment information, generating a second motion profile using the motion model, comparing the second motion profile to the first motion profile, and confirming when the second motion profile matches the first motion profile to confirm that the individual is at the location.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive operation data generated by first components of a vehicle; determine that the vehicle is being operated autonomously by an on-board autonomous-operation module based on the operation data without using output from the on-board autonomous-operation module; and upon determining that the vehicle is being operated autonomously, actuate a second component of the vehicle.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive lidar data from a lidar device, generate a point cloud from the lidar data, identify an object in the point cloud, and, upon identifying the object, delete a portion of the lidar data that is from a region encompassing the object. The object includes personally identifiable information.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a first time series of positions of a vehicle sensor mounted on a vehicle from at least one calibration sensor spaced from the vehicle, measured while vibrations are applied to the vehicle; receive a second time series of motion of the vehicle sensor based on data from the vehicle sensor measured while the vibrations are applied to the vehicle; fuse the first and second time series; and determine at least one calibration value for an adjustment model based on the fusion of the first and second time series. The adjustment model is a model of motion of the vehicle sensor relative to the vehicle resulting from motion of the vehicle.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to predict a quantity of misalignment of an optical sensor based on a projected motion of a vehicle, predict an error of the predicted quantity of misalignment, and actuate the vehicle based on the predicted quantity of misalignment and the predicted error. The vehicle includes the optical sensor.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive sensor data in a time series from a sensor, identify an object in the sensor data, generate anonymized data for the object at a first time in the time series based on the sensor data of the object at the first time, and apply the same anonymized data to an instance of the object in the sensor data at a second time in the time series. The object includes personally identifiable information.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a first time series of positions of a vehicle sensor mounted on a vehicle from at least one calibration sensor spaced from the vehicle, measured while vibrations are applied to the vehicle; receive a second time series of motion of the vehicle sensor based on data from the vehicle sensor measured while the vibrations are applied to the vehicle; fuse the first and second time series; and determine at least one calibration value for an adjustment model based on the fusion of the first and second time series. The adjustment model is a model of motion of the vehicle sensor relative to the vehicle resulting from motion of the vehicle.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive first speech data, remove a first vector of speaker-identifying characteristics from the first speech data to generate extracted first speech data, generate a random vector of the speaker-identifying characteristics, and generate second speech data by applying the random vector to the extracted first speech data.