An information processing device (100) includes a calibration execution unit (195) that performs calibration between two or more sensors which are attached at different positions and of which visual field regions at least partially overlap each other. The calibration execution unit (195) performs the calibration between the sensors in a case where a feature point that enables calibration of each of the two or more sensors is acquirable in a region in which visual fields of the two or more sensors overlap each other.

Among other things, techniques are described for identifying sensor data from a sensor of a first vehicle that includes information related to a pose of at least two other vehicles on a road. The technique further includes determining a geometry of a portion of the road based at least in part on the information about the pose of the at least two other vehicles. The technique further includes comparing the geometry of the portion of the road with map data to identify a match between the portion of the road and a portion of the map data. The technique further includes determining a pose of the first vehicle relative to the map data based at least in part on the match.

A computer comprises a processor and a memory. The memory stores instructions executable by the processor to, upon detecting an airbag deployment in a vehicle, determine an airbag inflation state based on vehicle sensor data, to adjust a calibration parameter of a vehicle radar sensor based on the determined airbag inflation state, to operate the vehicle radar sensor based on the adjusted calibration parameter, and to update an output characteristic of the radar sensor for operating the vehicle based on the determined airbag inflation state.

A radar sensor includes a memory storing a model defining a relationship between a condition of the radar sensor and a plurality of features of radar detections, the model being generated by a machine learning approach and storing values of the plurality of features associated with the known states of the condition of the radar sensor. A radar detector transmits radar signals into a region, detects reflected returning radar signals from the region, and converts the reflected returning radar signals into digital data signals. A processor receives the digital data signals and processes the digital data signals to generate actual radar detections, each characterized by a plurality of the features of radar detections. The processor applies values of the features of the actual radar detections to the model to determine the state of the condition of the radar sensor.

Technologies for steering sensors in a sensor carrier structure on an autonomous vehicle (AV) are described herein. In some examples, a sensor positioning platform on an AV can include an actuator system including a motor configured to move and reposition a sensor carrier structure having a plurality of sensors; a motor controller configured to receive instructions for controlling the motor to reposition the sensor carrier structure and, based on the instructions, send to the motor control signals configured to control the motor to reposition the sensor carrier structure; one or more hoses arranged within tubes mounted to a portion of the actuator system and configured to output sensor cleaning substances through a thru-bore on the actuator system; and one or more cleaning systems configured to receive the sensor cleaning substances and spray the sensor cleaning substances on one or more sensors on the sensor carrier structure.

Low-cost devices and methods for measuring radar transmission and/or reflectance of coated articles, as well as methods for forming coatings on articles are provided. An exemplary low-cost radar transmission and reflection measurement device includes a radar transmitter that emits a radar signal, a radar target to which the radar signal is directed, and a radar receiver that receives the radar signal. Further, the exemplary low-cost device includes a sample holder located between the radar transmitter and the radar target and between the radar target and the radar receiver. The sample holder receives a sample including a coating. The low-cost device also includes a controller connected to the radar transmitter and radar receiver. The controller measures a radar signal loss due to the coating.

A monitoring and alert system for a radar-based object detection device has a blockage detection circuit including elements that detect and warn of radar blockage from dirt or debris on a radome. Environmentally-applied or human-applied material becoming attached to a surface of the radome can block the radar signal from being radiated, or a target return/echo from being received, at a sufficient power level. The monitoring and alert system prevents a vehicle operator from assuming that the unit is functioning properly and that no targets exist in the radar field of view, when the radar is actually blocked by the dirt and debris. One electrode, or multiple spaced-apart electrodes, on the radome may be monitored for each electrode's respective “self-capacitance”. Thus, different regions of the radome may be separately and independently monitored for alerts when one or more regions of the radome are negatively-affected by the dirt or debris.

Methods and devices for estimating a component transmission loss are provided. In an exemplary embodiment, a method includes receiving a desired substrate criterion of a desired substrate, and receiving a desired coating criterion of a desired coating. A component includes the desired substrate and the desired coating. A coating criterion value is received, where the coating criterion value quantifies the desired coating criterion. A desired coating permittivity is estimated for the desired coating, using the coating criterion value, and an estimated component transmission loss of radar signal through the component is produced.

Devices, systems, and methods are provided for optical device validation. For example, a system may comprise an optical device operable to emit or absorb light, wherein the optical device comprises a lens having an outer surface. The system may comprise a camera positioned in a line of sight of the optical device, wherein the camera is operable to capture one or more images of the optical device. The system may comprise a computer system in communication with to the camera and operable to calculate a validation score for a captured image of the one or more images and to validate the optical device based on a validation state generated using the calculated validation score.

Methods and systems are disclosed for determining sensor degradation by actively controlling an autonomous vehicle. Determining sensor degradation may include obtaining sensor readings from a sensor of an autonomous vehicle, and determining baseline state information from the obtained sensor readings. A movement characteristic of the autonomous vehicle, such as speed or position, may then be changed. The sensor may then obtain additional sensor readings, and second state information may be determined from these additional sensor readings. Expected state information may be determined from the baseline state information and the change in the movement characteristic of the autonomous vehicle. A comparison of the expected state information and the second state information may then be performed. Based on this comparison, a determination may be made as to whether the sensor has degraded.