A computer-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. The method may also include recording image data collected from a camera sensor, the image data comprising a first 2D image frame comprising the simulated objects made up of a plurality of pixels. The method may also include identifying a first 3D point on the first simulated object in a 3D view of the simulated road scenario, wherein the first 3D point corresponds to the first pixel in the first 2D image frame. The method may also include generating a velocity of the first point based upon a velocity of the first simulated object, and projecting the velocity back into the first 2D image frame. The method may further include encoding the velocity for the first pixel prior to a simulated movement of the simulated object with respect to a second pixel after the simulated movement of the simulated object in a 2D velocity grid.

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.

A method of modelling a scanning distance sensor determining a set of detections is determined as if obtained by the sensor when scanning a field of view of the sensor, wherein each of the detections corresponds to a different line of sight originating from the sensor and comprises information about the orientation of the respective line of sight and about the distance of a respective target point from the sensor, the target point being the point in space where the line of sight first crosses any of the objects at the respective point in time. The method includes that the set of detections is modified by estimating the effect of sequentially scanning the field of view in discrete time steps on the detections and inversely applying the estimated effect to the set of detections.

A method of modeling precipitation effects in a virtual LiDAR sensor, the method includes receiving a point cloud model representing three-dimensional coordinates of objects as the objects would be sensed by a LiDAR sensor. The method further includes generating a stochastic model of rainfall or snowfall, estimating a probability that a light source from the LiDAR sensor hits a raindrop or a snowflake based on the stochastic model, and modifying the received point cloud model to include effects induced by the modeled rainfall or snowfall based on the probability that light sourced from the LiDAR sensor encounters a raindrop or a snowflake.

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.

There is a method for verifying accuracy of a virtual sensor model for simulation based on reality information data. According to an embodiment, a virtual sensor verification method may acquire information on positions and states of real vehicles which are running on a real road, may acquire real sensor data generated in real sensors of a reality information acquisition vehicle from among the real vehicles, may reproduce the real vehicles on a virtual road as virtual vehicles, based on the acquired information on the positions and states, may acquire virtual sensor data outputted from virtual sensors mounted in a virtual information acquisition vehicle from among the virtual vehicles, and may verify the virtual sensors by comparing the acquired real sensor data and the virtual sensor data. Accordingly, accuracy of virtual sensor data which is supplied to a recognition, determination, control algorithm for autonomous driving may be measured and verified, so that accuracy on a result of verifying based on a simulator of an autonomous driving algorithm may be enhanced.

Systems and techniques of the present disclosure may access data from a time-of-flight (TOF) sensor of an autonomous vehicle (AV). The TOF sensor may have light signals and received reflections of those transmitted signals such that a set of simulation data can be generated. This set of simulation data may identify a distance to associate with an object that is different from a calibration distance. Equations may be used to identify a light signal amplitude, a signal to noise ratio (SNR), and a range inaccuracy due to noise from the accessed data. The identified the light signal amplitude, the SNR, and the range inaccuracy due to noise may have been identified using equations. Once the set of simulation data is generated, it may be saved for later access by a processor executing a simulation program used to train devices used to control the driving of an AV.

The objective of the present invention is to provide a quantum receiver using square of homodyne detection for detecting a target of a quantum radar by using the square of homodyne detection that uses homodyne detection used in quantum information processing using continuous variables, and data processing, and a measurement method therefore. In order to achieve the above objective, the quantum receiver for detecting a target of a quantum radar using the square of homodyne detection according to the present invention comprises: a first 50:50 beam splitter for mixing signals coming into an input terminal; and two light quantity measurement units for measuring the quantity of light respectively outputted to two output terminals of the first 50:50 beam splitter.

A computer-implemented method is provided for simulating sensor data acquisition. The method may include receiving simulated sensor data corresponding with a synthetic object in a simulated three-dimensional (3D) environment. The method may also include identifying an object type corresponding with the synthetic object based on a location of the synthetic object in the simulated 3D environment. The method may further include associating an intensity value with the simulated sensor data based on the object type for the synthetic object.

A method for synthetically generating a point cloud of radar or LIDAR reflections, a reflection indicating at least one location at which radar or LIDAR interrogating radiation has been reflected. In the method, distribution functions which according to a random distribution provide samples in each case for at least one of the variables contained in the radar or LIDAR reflections are provided; synthetic reflections are generated by drawing samples in each case from the distribution functions for variables contained in the radar or LIDAR reflections, one of multiple distribution functions being selected according to at least one selection random distribution in order to draw each sample; the synthetic reflections are combined to form the sought point cloud.