A system for flight control system using simulator data for an electric aircraft is presented. The system includes a computing device, the computing device configured to receive a plurality of measured flight data, simulate a plurality of aircraft performance model outputs as a function of a flight simulator and the plurality of measured flight data, determine a moment datum as a function of the plurality of measured flight data and the plurality of aircraft performance model outputs, generate an allocation command datum as a function of the moment datum and the plurality of aircraft performance model outputs, and perform a torque allocation on a flight component of a plurality of flight components as a function of the allocation command and the moment datum.

Systems and methods to control gain of an electric aircraft are provided in this disclosure. The system may include gain scheduling to provide stability of the electric aircraft at various dynamic states of operation. The system may include a sensor to obtain measurement datum of an operating state. The system may further include a controller that adjusts a control gain of the electric aircraft as a function of the measurement datum. The gain control may be determined by a gain schedule generated by the controller.

Aspects of the disclosure provide for a method for identifying speed limit signs and controlling an autonomous vehicle in response to detected speed limit signs. The autonomous vehicle's computing devices identifies a speed limit sign in a vehicle's environment and a location and orientation corresponding to the speed limit sign. Then, the and orientation location of the speed limit sign is determined to not correspond to a pre-stored location and a pre-stored orientation of a speed limit sign that is pre-stored in map information. An effect zone of the speed limit sign is determined based on the location and orientation of the speed limit sign and characteristics of surrounding areas or other detected object before or after the speed limit sign. The autonomous vehicle's computing devices determines a response of the vehicle based on the determined effect zone, and controls the autonomous vehicle based on the determined response.

A method is provided. An unmanned aerial vehicle (UAV) is operated. A position of the UAV is determined while in flight, and a nonce is generated. A Merkel root is generated based at least in part on a timestamp and the position of the UAV. A current block is calculated based at least in part on a previous block, the Merkel root, and the nonce, and the current block, the timestamp, the nonce, the prior block, and the position of the UAV are transmitted.

The present disclosure provides a method including determining an operational mode of a vehicle based on data accumulated from at least one vehicle information system associated with the vehicle; selecting one of a plurality of power consumption profiles for the vehicle based on the determined operational mode; and applying the selected one of the power consumption profiles to the vehicle.

An autonomous driving controller includes a plurality of parallel processors operating on common input data. Each of the plurality of parallel processors includes a general processor, a security processor subsystem (SCS), and a safety subsystem (SMS). The general processors, the SCSs, and the SMSs of the plurality of parallel processors are configured to first, boot the plurality of SCSs from ROM second, boot the plurality of SMSs of the plurality of parallel processors from RAM or ROM, and, third, boot the plurality of general processors of the plurality of parallel processors from RAM. Between booting of the SCSs and the SMSs, at least one of the plurality of SCSs may load SMS boot code into the RAM that is dedicated to the plurality of SMSs.

An occlusion detection system for an autonomous vehicle is described herein, where a signal conversion system receives a three-dimensional sensor signal from a sensor system and projects the three-dimensional sensor signal into a two-dimensional range image having a plurality of pixel values that include distance information to objects captured in the range image. A localization system detects a first object in the range image, such as a traffic light, having first distance information and a second object in the range image, such as a foreground object, having second distance information. An occlusion polygon is defined around the second object and the range image is provided to an object perception system that excludes information within the occlusion polygon to determine a configuration of the first object. A directive is output by the object perception system to control the autonomous vehicle based upon occlusion detection.

An autonomous vehicle safety system may activate to prevent collisions by detecting that a planned trajectory may result in a collision. If the safety system is overly sensitive, it may cause false positive activations, and if the system isn't sensitive enough the collision avoidance system may not activate and prevent a collision, which is unacceptable. It may be impossible or prohibitively difficult to detect false positive activations of a safety system and it is unacceptable to risk a false negative, so tuning the safety system is notoriously difficult. Tuning the safety system may include detecting near-miss events using surrogate metrics, and tuning the safety system to increase or decrease a rate of near-miss events as a stand-in for false positives.

Aspects described herein include an autonomous surface vehicle (ASV) for operation within an inventory system of an environment. The ASV includes a drive system, a docking system, a plurality of sensors, and a memory storing a map of the environment. The ASV further includes one or more computer processors configured to (i) detect, using a location sensor, a location of the ASV within the environment; (ii) control the drive system to actuate the ASV toward a corridor defined in the map at a first speed setting; and control the drive system to actuate the ASV through the corridor along at least one barrier defined in the map. A second, greater speed setting is applied when (i) the location sensor indicates that the ASV is within the corridor and (ii) one or more fiducials along the at least one barrier are visually detected by one or more proximity sensors.

A response system may be provided. The response system may include a security system and an autonomous drone. The security system includes a security sensor and a controller. The drone includes a processor, a memory in communication with the processor, and a drone sensor. The processor may be programmed to receive the deployment request from the security system, navigate to the one or more zones of the coverage area included in the deployment request, collect drone sensor data of the one or more zones of the coverage area using the at least one drone sensor, determine that an incident has occurred, and/or transmit the collected drone sensor data and incident verification to the security system, wherein, in response to receiving the collected drone sensor data and incident verification, the security system is configured to generate a command for responding to the incident.