An adaptive region division method and system are provided. The adaptive region division method includes: building an environmental map based on laser radar data and odometer data, to determine information about an environment in which a target device is located (S11); performing feature extraction according to the laser radar data, to determine feature data, where the feature data includes line feature data and point feature data (S12); generating a virtual door according to the feature data and the information about the environment in which the target device is located (S13); and dividing a to-be-divided region where the target device is located according to the virtual door (S14). Therefore, a virtual door is generated according to laser data of a current environment to achieve the purpose of adaptive region division, so that the target device can more efficiently and quickly cover the whole space.

Examples disclosed herein relate to a range adaptable antenna system for use in autonomous vehicles. The antenna system has a connector and a transition layer to receive an RF transmission signal from a transmission signal controller, a range adaptable power divider layer coupled to the connector and transition layer to divide the RF transmission signal into a plurality of transmission signals to propagate through an array of transmission lines, with a set of transmission lines from the array of transmission lines having a set of switches, an RFIC layer having a plurality of phase shifters to apply different phase shifts to the plurality of transmission signals and generate a plurality of phase shifted transmission signals, and an antenna layer having an array of superelements for radiating the plurality of phase shifted transmission signals, wherein a set of superelements is connected to the set of switches in the range adaptable power divider layer for deactivation.

Examples relating to vehicle velocity calculation using radar technology are described. An example method performed by a computing system may involve, while a vehicle is moving on a road, receiving, from two or more radar sensors mounted at different locations on the vehicle, radar data representative of an environment of the vehicle. The method may involve, based on the data, detecting at least one scatterer in the environment. The method may involve making a determination of a likelihood that the at least one scatterer is stationary with respect to the vehicle. The method may involve, based on the determination being that the likelihood is at least equal to a predefined confidence threshold, calculating a velocity of the vehicle based on the data from the sensors. The calculated velocity may include an angular and linear velocity. Further, the method may involve controlling the vehicle based on the calculated velocity.

A system having a guidance module to obtain a travel path from an agricultural machine to a target. The guidance module including a perception module to determine target line that extends from the machine to the target, and obstacle detection module to detect an obstacle in the first target line, the obstacle having an obstacle boundary that intersects the target line. The guidance module including a mitigation module to obtain a mitigation path around the obstacle based on the target line and the obstacle boundary, the mitigation path including a mitigation segment. The mitigation module including an entrance module configured to determine a starting position of the mitigation path based on a second target line and an exit module configured to determine the ending position of the mitigation path based on a third target line. The guidance module including convergence module to combine the mitigation path with the travel path.

An ADV may determine whether there is preexisting map data for an environment or geographical area/location where the ADV is located/travelling. If there is no preexisting data, the ADV may generate map data based on sensor data obtained from one or more sensors of the ADV. The ADV may determine a path for the ADV based on the generated map data. If there is preexisting map data, the ADV may determine a path for the ADV based on the preexisting map data.

Automated training dataset generators that generate feature training datasets for use in real-world autonomous driving applications based on virtual environments are disclosed herein. The feature training datasets may be associated with training a machine learning model to control real-world autonomous vehicles. In some embodiments, an occupancy grid generator is used to generate an occupancy grid indicative of an environment of an autonomous vehicle from an imaging scene that depicts the environment. The occupancy grid is used to control the vehicle as the vehicle moves through the environment. In further embodiments, a sensor parameter optimizer may determine parameter settings for use by real-world sensors in autonomous driving applications. The sensor parameter optimizer may determine, based on operation of the autonomous vehicle, an optimal parameter setting of the parameter setting where the optimal parameter setting may be applied to a real-world sensor associated with real-world autonomous driving applications.

Systems, methods, computing platforms, and storage media for transporting a mobile inventory transportation unit (MITU) in a communication network are disclosed. Exemplary implementations may include the mobile inventory transportation communication network comprising the MITU, a transportation system, a first and a second central system, in communication with each other, the MITU comprising a housing, an inventory storage device, a power device, a drive device, a navigation device, a sensing device, and a control device. The transportation system may be configured to physically receive and transport the MITU from a first point to a second point, the second central system may be configured to determine an inventory demand at a second or more location and transmit inventory request data to the first central system, and the first central system may be configured to schedule the movement of the MITU and control the delivery of the MITU to a final destination.

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.

A method for identifying a trajectory between rows of a plantation using a radar interfaced with a processing means of an agricultural vehicle includes acquisition of an approximate distance between two consecutive rows of the plantation, acquisition of signals by the radar, processing of the signals to obtain a two-dimensional map of points corresponding to reflections picked up by the radar. The method further includes first linear interpolation to obtain a first interpolating line on the points of greatest intensity, second windowing of an elongated area of the two-dimensional map having an axis of development approximately parallel to the first interpolating line and at the approximate distance from the first interpolating line, second linear interpolation of a second interpolating line on points of greater intensity in the windowed area, and calculation of a trajectory parallel and intermediate between the first and second interpolating line.

Examples disclosed herein relate to a dynamic supply modulation power amplifier architecture for millimeter wave applications. The architecture includes phase shifters coupled to a power input port, power amplifiers coupled to respective power output ports, variable gain amplifiers coupled to the phase shifters and to the power amplifiers and are configured to supply dynamically varying input power to the power amplifiers. The architecture includes a first look-up table coupled to the variable gain amplifiers to control the variable gain amplifiers. The architecture also includes a second look-up table coupled to the power amplifiers, where each of the power amplifiers is supply modulated by active drain voltage modulation controlled by the second look-up table and variable input power from the variable gain amplifiers. Other examples disclosed herein include a radar system for use in an autonomous driving vehicle and an analog beamforming antenna for millimeter wave applications.