Systems and methods for operating radar systems. The methods comprise, by a processor: receiving point cloud information generated by at least one radar device and a spatial description for an object; generating a plurality of point cloud segments by grouping data points of the point cloud information based on the spatial description; arranging the point cloud segments in a temporal order to define a radar tentative track; performing dealiasing operations using the radar tentative track to generate tracker initialization information; and using the tracker initialization information to generate a track for the object.

Disclosed herein is a mover including an object detecting unit and either a masking processing unit or a transmission restricting unit as an additional processing unit. The object detecting unit detects an object based on a reception signal output from a receiver unit by having a transmitter unit transmit a scanning wave and by having the receiver unit receive a reflected wave, which is a component, reflected from the object, of the scanning wave. The masking processing unit performs masking processing of masking, in accordance with a timing at which, and/or a direction of incidence in which, a disturbance wave, not depending on the scanning wave, is incident on the receiver unit, a portion of the reception signal output from the receiver unit. The transmission restricting unit restricts a transmission range in which the transmitter unit transmits the scanning wave.

An object-detection system for an automated vehicle includes an object-detector, a receiver, and a controller. The object-detector detects detectable-objects proximate to a host-vehicle. The receiver receives an indication of an object-presence from other-transmitters proximate to the host-vehicle. The controller is in communication with the object-detector and the receiver. The controller is configured to operate the host-vehicle to avoid interference with a hidden-object when the hidden-object is not detected by the object-detector and the object-presence is indicated by at least two instances of the other-transmitters.

The present technology is directed to generating augmented data that are used for training a machine-learning (ML) algorithm to recognize objects under different weather conditions. The present technology may include receiving, by one or more processors, data of an environment including objects in a first geographical location. The data of the environment may be received from sensors on a vehicle moving on a road under a first weather condition. The present technology may also include receiving reference data that represent a second weather condition. The second weather condition may include a precipitation type. The present technology may also include generating augmented data including a subset of the reference data superimposed on the data of the environment. The augmented data simulates the environment under the second weather condition to simulate the environment under the second weather condition. The present technology may include providing the augmented data to an ML algorithm for training the ML algorithm to recognize the objects in the environment under the second weather condition.

A near-range camera assembly for a vehicle is provided. The near-range camera assembly includes a LIDAR mount couplable to a vehicle. The LIDAR mount includes a first surface and a second surface that opposes the first surface. The near-range camera assembly includes a LIDAR unit removably coupled to the first surface LIDAR mount. The near-range camera assembly further includes a camera and a camera housing for the camera. The camera housing is removably coupled to the second surface LIDAR mount.

Systems and methods for monitoring the lane of an object in an environment of an autonomous vehicle are disclosed. The methods include receiving sensor data corresponding to the object, and assigning an instantaneous probability to each of a plurality of lanes based on the sensor data as a measure of likelihood that the object is in that lane at a current time. The methods also include generating a transition matrix for each of the plurality of lanes that encode one or more probabilities that the object transitioned to that lane from another lane in the environment or from that lane to another lane in the environment at the current time. The methods then include determining an assigned probability associated with each of the plurality of lanes based on the instantaneous probability and the transition matrix as a measure of likelihood of the object occupying that lane at the current time.

A method for using Synthetic Aperture Radar (SAR) to perform a maneuver in a land vehicle is provided. The method includes: receiving digitized radar return data from a radar transmission from a SAR onboard the vehicle; accumulating a plurality of frames of the digitized radar return data; applying a RADON transform to the accumulated plurality of frames of the digitized radar return data and odometry data from the vehicle to generate transformed frames of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent integration for each three-dimensional point, project a radar trajectory onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generating a two-dimensional map of an area covered by the radar transmission from the SAR based on the transformed frames of data for each three-dimensional point; and performing a maneuver with the land vehicle by applying the generated two-dimensional map.

A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

This disclosure describes techniques for using radar cross-section (RCS) data to classify objects detected by autonomous vehicles within driving environments. In some examples, the variance of the RCS data associated with an object may be evaluated to determine signal interference caused by multipath fading. The variance of the RCS data may be used to classify the object and to determine whether the autonomous vehicle can safely drive over the object. For instance, objects such as manhole covers, storm drains, and expansion joints may provide a significant radar signal, but low RCS variance indicating that they can be driven over by the vehicle. Based on the classification of the object, the autonomous vehicle may determine a trajectory around the object or directly over the object.

Embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be used to generate map data. Additionally or alternatively, the RADAR point clouds may be used for performing localization.