An automated vehicle radar system capable of self-calibration includes an antenna, a transceiver, and a controller. The antenna broadcasts a radar-signal and detects a reflected-signal reflected by an object. The transceiver determines a distance, an angle, and a range-rate of the object relative to the antenna based on the radar-signal and the reflected-signal. The controller determines a speed of a host-vehicle; determines when the object is stationary based on the speed, the angle, and the range-rate; stores in a memory a plurality of detections that correspond to multiple instances of the distance, the angle, and the range-rate as the host-vehicle travels by the object; selects an ideal-response of angle versus range-rate based on the speed; determines a calibration-matrix of the system based on a difference between the plurality of detections and the ideal-response; and adjusts an indicated-angle to a subsequent-object in accordance with the calibration-matrix.

A method for ascertaining a transverse-velocity-component (TVC) of a radar-target (RT), including: periodically sending modulated, transmitted signals with a transmitting device (TD) having transmission-elements, into a sensing-region of the radar-device (RD), during a measuring-period; receiving a radar-signal (RS) reflected by the RT, using a receiving-device having receiving-elements; transmitting the received (RS) to an evaluation-device and converting it; performing a two-dimensional Fourier-transformation (FT) to generate a velocity-distance spectrum (VDS) of the digital-measured values for each transmission/receiving element; detecting a target reflection of the RT in light of, peak values in a magnitude-spectrum of the VDS; ascertaining a distance of the RT from the RD, and a radial-velocity-component relative to the RD, from the VDS; determining an angle of the RT relative to the antenna; selecting the RT; performing an inverse FT of the target reflection of the RT, and ascertaining the transverse-velocity-component of the RT from the transformed-measured values.

A process and systems for constructing arbitrarily large virtual arrays using two or more collection platforms (e.g. AUX and MOV systems) having differing velocity vectors. Referred to as Motion Extended Array Synthesis (MXAS), the resultant imaging system is comprised of the collection of baselines that are created between the two collection systems as a function of time. Because of the unequal velocity vectors, the process yields a continuum of baselines over some range, which constitutes an offset imaging system (OIS) in that the baselines engendered are similar to those for a real aperture of the same size as that swept out by the relative motion, but which are offset by some (potentially very large) distance.

A system determines a dynamic collision awareness envelope for a vehicle. The system includes at least one vehicle motion sensor, an operator Line-Of-Sight detector and a processor. The vehicle motion sensor periodically provides measurements relating to the motion of the vehicle in a reference coordinate system. The operator Line-Of-Sight detector periodically provides information relating to the direction of the Line-Of-Sight of an operator of the vehicle, in a vehicle coordinate system. The processor is coupled with the at least one vehicle motion sensor, and with the operator Line-Of-Sight detector. The processor determines an operator vector from the direction of the Line-Of-Sight of the operator. The processor further determines an operational vector at least from the motion of the vehicle. The processor periodically determines a collision awareness envelope respective of each of the operational vectors, from the operator vector and the respective operational vector.

A method for determining a movement vector of a motor vehicle includes: determining, via a radar system of the vehicle, at two successive instants, positions, relative to the vehicle, of elements of an environment of the vehicle that are static relative to the environment, associating the positions determined at these two successive instants with each other in such a way as to form different pairs of positions each grouping together the preceding position and the subsequent position of a given element of the environment, and determining the movement vector of the vehicle by linear regression, based on the pairs of positions thus formed.

Embodiments of the present disclosure are directed to a method for object detection. The method includes receiving sensor data indicative of one or more objects for each of a camera subsystem, a LiDAR subsystem, and an imaging RADAR subsystem. The sensor data is received simultaneously and within one frame for each of the subsystems. The method also includes extracting one or more feature representations of the objects from camera image data, LiDAR point cloud data and imaging RADAR point cloud data and generating image feature maps, LiDAR feature maps and imaging RADAR feature maps. The method further includes combining the image feature maps, the LiDAR feature maps and the imaging RADAR feature maps to generate merged feature maps and generating object classification, object position, object dimensions, object heading and object velocity from the merged feature maps.

Disclosed is a method and apparatus for processing a radar signal by correcting a phase distortion. The method includes generating radar data based on a radar transmission signal transmitted through an array antenna of a radar sensor based on a frequency modulation model and a radar reception signal received through the array antenna as the radar transmission signal is reflected by a target, correcting the radar data using a correction vector for correcting a feedline error occurring due to a feedline delay difference between channels of the array antenna, and estimating a direction of arrival corresponding to the corrected radar data using a direction matrix reflecting a phase shift of the corrected radar data according to frequency modulation characteristics of the frequency modulation model.

A method for detecting critical transverse movements. The method includes the following steps: emitting a CW radar signal and generating radar data based on the received reflected CW radar signal with the aid of a radar device; ascertaining collision-relevant spectral ranges of the radar data as a function of an ego velocity of the radar device; ascertaining a time dependency of a relative velocity and of an object angle of an object by evaluating the radar data in the ascertained spectral ranges; and detecting a critical transverse movement of the object using the time dependency of the relative velocity and of the object angle of the object.

An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

Embodiments of the present disclosure provide a method, an apparatus, a device, and a medium for determining an angle of yaw, relating to a field of automatic driving. The method includes: obtaining, during a vehicle being driving straightly on a straight road, data of each obstacle in an environment located by the vehicle, the data of each obstacle being detected by a millimeter wave radar sensor located in the vehicle, at least one metal obstacle being provided on the straight road; recognizing the metal obstacle based on the data of each obstacle, and obtaining a metal obstacle line by fitting positions of the metal obstacle at different time points; and determining an angle between the metal obstacle line and a direction of a vehicle body as an angle of yaw between the millimeter wave radar sensor and the vehicle body.