Abstract: Multi-frequency microDoppler fusion is described. An example of a method includes performing a 2D Fourier transform of raw Radar sensor data and raw LiDAR sensor data to generate Radar range-Doppler map data and LiDAR range-Doppler map data; performing a Stationary Wavelet Transform (SWT) calculation of the Radar and LiDAR sensor range-Doppler map data to generate a Radar SWT product and a LiDAR SWT product; performing an SWT product thresholding operation to extract signatures that are common to the Radar SWT product and the LiDAR SWT product; and performing a microDoppler calculation on the extracted common signatures to generate Radar-LIDAR joint fused micro-Doppler signatures.

Abstract: A current set of radar data may be combined with previous sets of radar data to create a combined set of radar data. Each of these sets of radar data and the combined set of radar data may include various data points and each of these data points may be associated with certain specific features or classifications. The set of combined data may then be pre-processed to identify distances to associate with certain data points, locations to associate with those data points, and velocities associated with the data points such that each of the respective data points may be mapped and tagged with data that identifies identify positions, velocities, and other physical details of the respective data points. This pre-processed data may then be processed by a machine learning process to identify kinematic information that may then be provided to a tracking system of an AV.

Abstract: The present disclosure is directed to combining the strengths of different methods of analyzing collected sensor data to updating a driving pattern of an automated vehicle (AV). This may include combining data from sets of data that track movement of objects over time with instantaneously received sensor data based on a series of steps that include accessing data that tracks the motion of objects in the field of view of a sensing apparatus, receiving current sensor data that includes a component of current or instantaneous object motion, and identifying whether controls of AV should be maintained or changed. When controls of the AV are maintained, an AV may be controlled to stay in driving in a same lane of a roadway at a same velocity. When controls of an AV are changed, changes may include applying, increasing a velocity, or altering the course of the AV.

Abstract: Depth data processing systems and methods are disclosed. A mapping system receives, from one or more depth sensors, depth sensor data that includes a plurality of points corresponding to an environment. The mapping system uses one or more trained machine learning models to perform semantic segmentation of the plurality of points, to classify a first subset of the points into a first category and to classify a second subset of the points into a second category. The mapping system clusters the plurality of points into a plurality of clusters based on the semantic segmentation. At least some of the first subset of the points are clustered into a first cluster and at least some of the second subset of the points are clustered into a second cluster. The mapping system generates a map of at least a portion of the environment based on the plurality of clusters.

Abstract: Methods and apparatuses disclosed within provide a solution to problems associated with sensing apparatus of an automated vehicle (AV) not being able to adequately evaluate risks associated with objects that are not characteristic with a given driving environment. A sensing apparatus tuned to drive at quickly down highway may not adequately identify risks associated with pedestrians walking along that highway. A solution to this problem involves configuring a sensing apparatus to operate in two different modes, for example, a highway mode and a pedestrian mode at virtually the same time. This may include allocating a first percentage of processing resources to a highway mode and allocating a second percentage of processing resources to a pedestrian mode. When objects along the highway are consistent with a pedestrian, additional processing resources of the sensing apparatus may be allocated to the pedestrian mode to reduce a risk of impacting the pedestrian.

Abstract: The present disclosure is directed to a plurality of different software models allowing a processor to perform calculations associated with different sets of criteria using data associated with variables that have a strong correlation with one or more of the different criteria sets. Methods consistent with the present disclosure may use several different sets of software that include instructions associated with the collection and evaluation of data associated with a vehicle and with an automated driving system. Different criteria sets may be associated with a range of operating modes, spectral content of received signals, phases of radar signals, and an angular coverage/field of view of the radar apparatus. Results generated by each of the software models may allow a processor to assign weights to the results generated by the different models to generate a combined result that in turn is used to update an operational mode of the radar apparatus.

Abstract: Some radar sensors may provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor. Techniques for determining a two-or-more-dimensional velocity from one or more radar measurements associated with an object may comprise determining a data structure that comprises a yaw assumption and a set of weights to tune the influence of the yaw assumption. Determining the two-or-more-dimensional velocity may further comprise using the data structure as part of regression algorithm to determine a velocity and/or yaw rate associated with the object.

Abstract: Techniques for updating data operations in a perception system are discussed herein. A vehicle may use a perception system to capture data about an environment proximate to the vehicle. The perception system may receive state data stored in cyclic buffer of globally registered detection and occasionally converted to gridded point cloud in a local reference frame. The two-dimensional gridded point cloud may be processed using one or more neural networks to generate semantic data associated with a scene or physical environment surrounding the vehicle such that the vehicle can make environment aware operational decisions, which may improve reaction time(s) and/or safety outcomes of the autonomous vehicle.

Abstract: Techniques are described for determining a likelihood that a radar device failed to detect an object (i.e., a false negative). Determining the likelihood may be based at least in part on determining an estimated noise floor based at least in part on at least a portion of radar data, which may comprise one or more detections, and determining a likelihood that the portion of radar data includes a false positive, based at least in part on the estimated noise floor and a response profile associated with an object. A response profile may identify a received signal power and/or radar cross section associated with an object type.

Abstract: Some radar sensors may provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor. Techniques for determining a two-or-more-dimensional velocity from one or more radar measurements associated with an object may comprise determining a data structure that comprises a yaw assumption and a set of weights to tune the influence of the yaw assumption. Determining the two-or-more-dimensional velocity may further comprise using the data structure as part of regression algorithm to determine a velocity and/or yaw rate associated with the object.

Abstract: Techniques for updating data operations in a perception system are discussed herein. A vehicle may use a perception system to capture data about an environment proximate to the vehicle. The perception system may receive state data stored in cyclic buffer of globally registered detection and occasionally converted to gridded point cloud in a local reference frame. The two-dimensional gridded point cloud may be processed using one or more neural networks to generate semantic data associated with a scene or physical environment surrounding the vehicle such that the vehicle can make environment aware operational decisions, which may improve reaction time(s) and/or safety outcomes of the autonomous vehicle.