Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

A layer configuration prediction method is provided and includes: a specimen production step of producing multiple specimens by depositing layers of a material in configurations different from each other; a specimen measurement step of performing, on each specimen, measurement to acquire a texture parameter corresponding to a texture; a learning step of causing a computer to perform machine learning of a relation between each of the specimens and the texture parameter; a setting parameter calculation step of calculating a setting parameter corresponding to the texture set to a computer graphics image; and a layer configuration acquisition step of providing the setting parameter as an input to the computer having been caused to perform the machine learning, and acquiring an output representing the layering pattern of layers of the material corresponding to the setting parameter.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

A fixture that includes an electro-mechanical (EM) element; a communication interface; a processor; and a computer-readable storage media coupled to the processor and having instructions stored thereon which, when executed by the processor, cause the processor to perform operations comprising: receiving, from a service via the communication interface, parameters for scheduling an operation of the fixture; determining, based on the parameters, a plurality of commands for the EM element and a respective time to execute each of the commands; providing, at the respective time, each of the commands to the respective EM element for execution; providing, via the communication interface, an indication of completion of the operation of the fixture to the service.

A control device has a learning model that outputs an estimated value of a second physical quantity that is unobservable under a condition of manufacturing a SiC crystal, from a first physical quantity that is observable under the condition of manufacturing the SiC crystal. The control device generates a basic learning model by mechanical learning using, as teacher data, a simulation result of a simulation model based on structural data of a SiC crystal manufacturing apparatus. The control device acquires measured values of the first physical quantity and the second physical quantity measured under a condition that the SiC crystal is unable to be manufactured while the second physical quantity is observable, and generates the learning model that corrects an output of the basic learning model based on the measured values.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

Tools and techniques are described to automate commissioning of physical spaces. Controllers have access to databases of the devices that are controlled by them, including wiring diagrams and protocols, such that the controller can automatically check that each wire responds correctly to stimulus from the controller. Controllers also have access to databases of the physical space such that they can check that sensors in the space record the correct information for device activity, and sensors can cross-check each other for consistency. Once a physical space is commissioned, incentives can be sought based on commissioning results.

The subject matter of this specification can be embodied in, among other things, a method for controlling a turbine engine that includes receiving a predetermined arming threshold signal, receiving a predetermined triggering threshold signal, receiving a periodic signal from a speed sensor, determining a frequency signal based on the periodic signal, the predetermined arming threshold signal, and the predetermined triggering threshold signal, determining a speed value based on the determined frequency signal, and controlling a speed of a turbine based on the determined speed value.

A setpoint adjustment apparatus for a displacement meter (10) includes a determiner (343) to determine whether a measurement value acquired by an acquirer (341) in measurement of a reference workpiece using an applying setpoint, to be used in measurement of the reference workpiece, is within the range of a desired measurement value (352), and a changer (345) to change the applying setpoint. When the measurement value is within the range of the desired measurement value (352), the applying setpoint used in acquisition of the measurement value is employed as an applying setpoint for inspection of a measurement target (1). When the measurement value is out of this range, the applying setpoint used in acquisition of the measurement value is changed to a different applying setpoint, and whether the measurement value from the reference workpiece using this applying setpoint is within the range of the desired measurement value (352) is determined.

Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.