A radar system is described. The system comprises a radiation transmission unit, a radiation collection unit, and a processing unit. The radiation transmission unit is configured to generate electromagnetic radiation formed by a plurality of quantum entangled photons comprising first transmitted photon (signal) and second reference photon (idler). The radiation transmission unit transmits the first transmitted photons toward a region to be inspected and measures the second reference photons to obtain and store measured data thereof. The radiation collection unit comprises at least one radiation collection element configured to receive photons reflected from one or more objects in said region and generate data indicative of one or more parameters of the collected photons. The processing unit is configured to receive stored measured data on the second reference photons and data on parameters of the collected photons from the radiation collection unit, and to determine correlation between the stored measured data and the collected photons to thereby differentiate between noise collected photons and reflection of said first transmitted photons from one or more objects in the region to be inspected.

Spectral components of waves having one or more properties other than phase and amplitude that vary monotonically with time at a receiver, and provide retardations or lags in the variation in proportion to the times or distances traveled from the sources of the waves to the receiver. The lags denote the property values prior to departure from a source and are absent in its proximity. Orthogonality of the lags to modulated information makes them useful for ranging and for separation or isolation of signals by their source distances. Lags in frequencies and wavelengths permit multiplication of capacities of physical channels. Constancy of the lagging wavelengths along the entire path from a source to the receiver enables reception through channels or media unusable at the source wavelengths, as well as imaging at wavelengths different from the illumination.

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a parked vehicle with its engine running. The location of the parked vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of the parked vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a parked vehicle with its engine running based on actual sensor outputs input to the machine learning model.

A hybrid LiDAR system may include a long-range LiDAR subsystem characterized by a first range and a first azimuth angular coverage, and a short-range LiDAR subsystem characterized by a second range and a second azimuth angular coverage, wherein the first range is greater than the second range, and the second azimuth angular coverage is greater than the first azimuth angular coverage.

Disclosed are systems, apparatuses, methods, and computer-readable media to autonomous driving vehicles and, in particular, for tracking objects in an environment that an autonomous vehicle (AV) is navigating. A method includes receiving environment data from at least one sensor in an AV control system mounted to the AV, the environment data including online tracking data that identifies at least one object within the environment data that is recorded at drive time; annotating the at least one object from the environment data that are incorrectly identified by the AV control system; executing an offline tracking engine to generate offline tracking data that tracks the objects over time in the environment data; and identifying safety gaps between the online tracking data and the offline tracking data.

An image processing method for reducing distortion of a depth image may include: obtaining a plurality of original images based on light beams which are emitted to and reflected from a subject; determining original depth values of original depth images obtained from the plurality of original images, based on phase delays of the light beams, the reflected light beams comprising multi-reflective light beams that distort the original depth values; determining imaginary intensities of the multi-reflective light beams with respective to each phase of the multi-reflective light beams, based on regions having intensities greater than a predetermined intensity in the original depth images; correcting the original depth values of the original depth images, based on the imaginary intensities of the multi-reflective light beams; and generating corrected depth images based on the corrected original depth values.

The present invention relates to a method implementing measurements obtained with a LiDAR sensor (2) mounted on a wind turbine (1), and measurements obtained with at least one motion sensor (CAM), as well as a LiDAR measurement model (MOD M) and a wind model (MOD V). The method then implements an informative adaptive Kalman filter (KAL) to determine the wind speed (v) at some estimation points. At least one wind speed characteristic (CAR) can then be possibly deduced therefrom, in the rotor plane for example.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as wheel stops or parking barriers. A method for generating virtual sensor data includes simulating a three-dimensional (3D) environment comprising one or more objects. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth includes information about at least one object within the virtual sensor data. The method also includes storing and associating the virtual sensor data and the virtual ground truth.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as wheel stops or parking barriers. A method for generating virtual sensor data includes simulating a three-dimensional (3D) environment comprising one or more objects. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth includes information about at least one object within the virtual sensor data. The method also includes storing and associating the virtual sensor data and the virtual ground truth.