A computer-implemented method is provided for creating a velocity grid of a simulated object in a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects. The method may also include tracking a point representative of a portion of one of the simulated objects in the simulated road scenario, wherein the point representative of the portion of one of the simulated objects moves from a first location to a second location in a time period, wherein the velocity of the point is generated from the first location to the second location. The method may further include storing the velocity of the point representative of the portion of one of the simulated objects in a velocity grid for the simulated objects in a memory device in an electrical communication with a processor.

A computed-implemented method is provided for creating a velocity grid using a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects, collecting first LiDAR data from simulated LiDAR sensors in the simulated road scenario, wherein the collected first LiDAR data comprises a first plurality of points that are representative of a first simulated object at a first 3D location and a first time. The method may also include transforming the first plurality of points from a simulated-scene frame-of-reference to a first simulated object frame-of-reference, and simulating the first simulated object to move from the first 3D location to a second 3D location within the simulated road scenario between the first time and a second time. The method may also include collecting second LiDAR data at the second time and transforming the second plurality of points from the first simulated object frame-of-reference back to the simulated-scene frame-of-reference. The method may also include calculating the velocity of the portion on the first simulated object in the simulated-scene frame-of-reference during a simulated movement of the first simulated object from the first 3D location to the second 3D location.

The subject disclosure relates to techniques for generating localized atmospheric phenomena in a simulated environment. A process of the disclosed technology can include generating a plurality of volumetric sequences, generating a corresponding plurality of sequence slices for each of the plurality of volumetric sequences, and compiling the plurality of volumetric sequences to generate a synthetic localized atmospheric event.

In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

A lidar map-based loop detection method, an electronic device, and a storage medium, which are related to a field of intelligent transportation and a technical field of automatic driving. The specific implementation include: acquiring an eigenvector of each grid in each sub-map of N sub-maps of the lidar map; determining a target eigenvector of each grid in each sub-map of the N sub-maps according to the eigenvector of each grid in each sub-map of the N sub-maps; constructing histograms of the N sub-maps according to the target eigenvector of each grid in each sub-map of the N sub-maps; and determining that a loop relation exists between two target sub-maps in the N sub-maps, in case that a similarity of histograms of the two target sub-maps is greater than a preset threshold value.

A LADAR system includes a transmitter configured to emit a directed optical signal. The LADAR system includes a shared optical aperture through which the directed optical signal is emitted. The shared optical aperture includes a first pupil plane. The shared optical aperture receives a return optical signal that is based on the directed optical signal. The system includes a mirror with a hole through which the directed optical signal passes. The mirror also reflects the return optical signal towards an imager. The imager receives the return optical signal and generates an image. The image is based on a portion of the return optical signal. The system also includes a partial aperture obscuration at a second pupil plane. The partial aperture obscuration may block a portion of internal backscatter in the return optical signal. The system also includes a focal plane to record the image.

Disclosed herein are examples of ladar systems and methods where data about a plurality of ladar returns from prior ladar pulse shots gets stored in a spatial index that associates ladar return data with corresponding locations in a coordinate space to which the ladar return data pertain. This spatial index can then be accessed by a processor to retrieve ladar return data for locations in the coordinate space that are near a range point to be targeted by the ladar system with a new ladar pulse shot. This nearby prior ladar return data can then be analyzed by the ladar system to help define a parameter value for use by the ladar system with respect to the new ladar pulse shot. Examples of such adaptively controlled parameter values can include shot energy, receiver parameters, shot selection, camera settings, and others.

Techniques are described for time-of-fly sensors with hybrid refractive gradient-index optics. Some embodiments are for integration into portable electronic devices with cameras, such as smart phones. For example, a time-of-fly (TOF) imaging subsystem can receive optical information along an optical path at an imaging plane. A hybrid lens can be coupled with the TOF imaging subsystem and disposed in the optical path so that the imaging plane is substantially at a focal plane of the hybrid lens. The hybrid lens can include a less-than-quarter-pitch gradient index (GRIN) lens portion, and a refractive lens portion with a convex optical interface. The portions of the hybrid lens, together, produce a combined focal length that defines the focal plane. The hybrid lens is designed so that the combined focal length is less than a quarter-pitch focal length of the GRIN lens portion and has less spherical aberration than either lens portion.

Optical modality configured to simulate measurements of the radar cross-section of targets, dimensioned to be conventionally-measured in the RF-portion of the electromagnetic spectrum, with sub-micron accuracy. A corresponding compact optical system, with a foot-print comparable with a tabletop, employing optical interferometric time-of-flight approach to reproduce, on a substantially shorter time-scale, radar-ranging measurements ordinarily pertaining to the range of frequencies that are at least 10.sup.3 times lower than those employed in the conventional RF-based measurement.

Optical modality configured to simulate measurements of the radar cross-section of targets, dimensioned to be conventionally-measured in the RF-portion of the electromagnetic spectrum, with sub-micron accuracy. A corresponding compact optical system, with a foot-print comparable with a tabletop, employing optical interferometric time-of-flight approach to reproduce, on a substantially shorter time-scale, radar-ranging measurements ordinarily pertaining to the range of frequencies that are at least 10.sup.3 times lower than those employed in the conventional RF-based measurement.