The present disclosure relates to a radar device, including a transmitter circuit configured to generate an RF oscillator signal and to transmit an RF fault detection signal based on the RF oscillator signal, a receiver circuit configured to receive an RF reception signal based on the RF fault detection signal and to mix the RF reception signal with the RF oscillator signal in order to obtain a down-converted reception signal, and a fault detection circuit configured to detect a hardware fault of the radar device based on a phase of the down-converted reception signal.

A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

In an apparatus for detecting a target using a radar according to one aspect of the present invention, a first radar and a second radar, which are multi-channel radars each including a plurality of transmitting antennas and a plurality of receiving antennas, are installed to be spaced apart from each other, and position information of a target and velocity vector information of the target are calculated from first position information and first velocity information of the target acquired from the first radar and second position information and second velocity information of the target acquired from the second radar and then are used to detect and track the target.

Example embodiments relate to radar reflection filtering using a vehicle sensor system. A computing device may detect a first object in radar data from a radar unit coupled to a vehicle and, responsive to determining that information corresponding to the first object is unavailable from other vehicle sensors, use the radar data to determine a position and a velocity for the first object relative to the radar unit. The computing device may also detect a second object aligned with a vector extending between the radar unit and the first object. Based on a geometric relationship between the vehicle, the first object, and the second object, the computing device may determine that the first object is a self-reflection of the vehicle caused at least in part by the second object and control the vehicle based on this determination.

Provided is a sensor recognition integration device capable of reducing the load of integration processing so as to satisfy the minimum necessary accuracy required for vehicle travel control, and capable of improving processing performance of an ECU and suppressing an increase in cost. A sensor recognition integration device B006 that integrates a plurality of pieces of object information related to an object around an own vehicle detected by a plurality of external recognition sensors includes: a prediction update unit 100 that generates predicted object information obtained by predicting an action of the object; an association unit 101 that calculates a relationship between the predicted object information and the plurality of pieces of object information; an integration processing mode determination unit 102 that switches an integration processing mode for determining a method of integrating the plurality of pieces of object information on the basis of a positional relationship between a specific region (for example, a boundary portion) in an overlapping region of detection regions of the plurality of external recognition sensors and the predicted object information; and an integration target information generation unit 104 that integrates the plurality of pieces of object information associated with the predicted object information on the basis of the integration processing mode.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D flight tracking of objects includes, in at least one aspect, a method including obtaining two dimensional image data of a golf ball in flight, the two dimensional image data originating from a camera; obtaining radar data of the golf ball in flight, the radar data originating from a Doppler radar device associated with the camera; interpolating the radar data to generate interpolated radar data of the golf ball in flight; and blending radial distance information derived from the interpolated radar data of the golf ball in flight with angular distance information derived from the two dimensional image data of the golf ball in flight to form three dimensional location information of the golf ball in three dimensional space.

A method for evaluating an angular position of an object recognized on the basis of radar data, the radar data being ascertained by a radar device. The method includes: ascertaining of an intrinsic speed of the radar device; ascertaining a relative speed of the recognized object in relation to the radar device, using the ascertained radar data; ascertaining at least one angular test region using the ascertained intrinsic speed and the ascertained relative speed, the at least one angular test region corresponding to possible stationary objects that have a relative speed that substantially corresponds to the ascertained relative speed; and ascertaining whether an azimuth angle of the recognized object lies in the ascertained angular test region.

A method for evaluating an angular position of an object recognized on the basis of radar data, the radar data being ascertained by a radar device. The method includes: ascertaining of an intrinsic speed of the radar device; ascertaining a relative speed of the recognized object in relation to the radar device, using the ascertained radar data; ascertaining at least one angular test region using the ascertained intrinsic speed and the ascertained relative speed, the at least one angular test region corresponding to possible stationary objects that have a relative speed that substantially corresponds to the ascertained relative speed; and ascertaining whether an azimuth angle of the recognized object lies in the ascertained angular test region.

An apparatus including at least one emitter configured to emit energy; at least one receiver configured to receive the emitted energy, where the at least one emitter is mounted on at least one of: a robot arm, an end effector of the robot arm, a substrate on the robot arm, or a substrate process module, where the at least one receiver is mounted on at least one of: the robot arm, the end effector of the robot arm, the substrate on the robot arm, or the substrate process module.

Aspects described herein provide sensor data stream processing for enabling camera/radar sensor fusion, with application to road user detection in the context of Autonomous Driving/Assisted Driving (ADAS). In particular, a scheme to extract Region-of-Interests (ROI) from a high-resolution, high-dimensional radar data cube that can then be transmitted to a sensor fusion unit is described. The ROI scheme allows to extract relevant information, thus reducing the latency and data transmission rate to the sensor fusion module, without trading-off accuracy and detection rates. The sensor data stream processing comprises receiving a first data stream from a radar sensor, forming a point cloud by extracting 3D points from the 3D data cube, performing clustering on the point cloud in order to identify high-density regions representing one or ROIs, and extracting one or more 3D bounding boxes from the 3D data cube corresponding to the one or more ROIs and classifying each ROI.