Abstract: Example embodiments relate to real-time health monitoring for automotive radars. A computing device may receive radar data from multiple radar units that have partially overlapping fields of view and detect a target object located such that the radar units both capture measurements of the target object. The computing device may determine a power level representing the target object for radar data from each radar unit, adjust these power levels, and determine a power difference between them. When the power difference exceeds a threshold power difference, the computing device may perform a calibration process to decrease the power difference below the threshold power difference or alert the vehicle, including onboard algorithms, to the reduced performance of the radar.

Abstract: Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

Abstract: Example embodiments relate to real-time health monitoring for automotive radars. A computing device may receive radar data from multiple radar units that have partially overlapping fields of view and detect a target object located such that the radar units both capture measurements of the target object. The computing device may determine a power level representing the target object for radar data from each radar unit, adjust these power levels, and determine a power difference between them. When the power difference exceeds a threshold power difference, the computing device may perform a calibration process to decrease the power difference below the threshold power difference or alert the vehicle, including onboard algorithms, to the reduced performance of the radar.

Abstract: Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

Abstract: Example embodiments relate to techniques for detecting adverse road conditions using radar. A computing device may generate a first radar representation that represents a field of view for a radar unit coupled to a vehicle and during clear weather conditions and store the first radar representation in memory. The computing device may receive radar data from the radar unit during navigation of the vehicle on a road and determine a second radar representation based on the radar data. The computing device may also perform a comparison between the first radar representation and the second radar representation and determine a road condition for the road based on the comparison. The road condition may represent a quantity of precipitation located on the road and provide control instructions to the vehicle based on the road condition for the road.

Abstract: Example embodiments relate to techniques for detecting adverse road conditions using radar. A computing device may generate a first radar representation that represents a field of view for a radar unit coupled to a vehicle and during clear weather conditions and store the first radar representation in memory. The computing device may receive radar data from the radar unit during navigation of the vehicle on a road and determine a second radar representation based on the radar data. The computing device may also perform a comparison between the first radar representation and the second radar representation and determine a road condition for the road based on the comparison. The road condition may represent a quantity of precipitation located on the road and provide control instructions to the vehicle based on the road condition for the road.

Abstract: Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

Abstract: Example embodiments relate to techniques for detecting adverse road conditions using radar. A computing device may generate a first radar representation that represents a field of view for a radar unit coupled to a vehicle and during clear weather conditions and store the first radar representation in memory. The computing device may receive radar data from the radar unit during navigation of the vehicle on a road and determine a second radar representation based on the radar data. The computing device may also perform a comparison between the first radar representation and the second radar representation and determine a road condition for the road based on the comparison. The road condition may represent a quantity of precipitation located on the road and provide control instructions to the vehicle based on the road condition for the road.

Abstract: Electronic devices and a method of providing electronic warfare (EW) data in an encapsulated architecture in a vehicle are generally described. Emitters targeting the vehicle during a mission may be detected and an observable history of each emitter obtained as a function of time. Properties of each emitter may be both inferred based on the observable history and extracted from locally-stored pre-mission intelligence. The emitter properties, as well as current and historical state and threat level of the emitter and effectiveness of various countermeasures may be stored in an adaptive radar model (ARM) for the emitter. Each emitter may have its own ARM. The ARM may be used to take appropriate countermeasures for a particular emitter, based on the emitter alone or taking into account all of the emitters.

Abstract: Electronic devices and a method of providing electronic warfare (EW) data in an encapsulated architecture in a vehicle are generally described. Emitters targeting the vehicle during a mission may be detected and an observable history of each emitter obtained as a function of time. Properties of each emitter may be both inferred based on the observable history and extracted from locally-stored pre-mission intelligence. The emitter properties, as well as current and historical state and threat level of the emitter and effectiveness of various countermeasures may be stored in an adaptive radar model (ARM) for the emitter. Each emitter may have its own ARM. The ARM may be used to take appropriate countermeasures for a particular emitter, based on the emitter alone or taking into account all of the emitters.