Abstract: Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

Abstract: Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

Abstract: Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

Abstract: Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

Abstract: A method includes determining temperature values for roadway areas ahead of a vehicle, determining lubricant state values for the roadway areas, and determining lubricant thickness values for the roadway areas. The method also includes determining a tire-road friction estimate for each of the roadway areas using the temperature values, the lubricant state values, and the lubricant thickness values, and defining a friction map that relates the tire-road friction estimates to the roadway areas. The method also includes determining a motion plan based at least in part on the friction map, and controlling the vehicle based on the motion plan.

Abstract: A system such as a vehicle system, building, or electrical equipment may be provided with one or more optical components. The optical components may include a near-infrared camera or other components that operate at near-infrared wavelengths. A visible-light-reflecting-and-infrared-light-transmitting layer may overlap the optical component. The visible-light-reflecting-and-infrared-light-transmitting layer may have an infrared-transparent substrate. A polymer layer may be formed on the substrate and may contain plasmonic nanoparticles that reflect white light. Colorant may be incorporated into the polymer layer or into an additional polymer coating to impart a desired color to the reflected white light and thereby provide the visible-light-reflecting-and-infrared-light-transmitting layer with a desired appearance.

Abstract: A system such as a vehicle system, building, or electrical equipment may be provided with one or more optical components. The optical components may include a near-infrared camera or other components that operate at near-infrared wavelengths. A visible-light-reflecting-and-infrared-light-transmitting layer may overlap the optical component. This overlapping layer may have first and second index-matched layers and an interposed textured layer. The textured layer may be a thin-film interference filter or other coating that is configured to reflect visible light while transmitting infrared light. The transmitted infrared light may pass to the optical component with minimal wavefront distortion due to the index matching of the first and second layers. The texture of the textured layer may cause visible light to reflect diffusely and thereby provide the visible-light-reflecting-and-infrared-light-transmitting layer with a matte appearance.