317results about "Control safety arrangements" patented technology

A sensor data evaluation device may include an artificial neural network, configured to receive and process first testing data, and output a first testing data output, wherein the first testing data represent sensor data of a first sensor detecting a sensor input; and one or more processors, configured to receive sensor deficiency information representing a deficiency of a second sensor; generate second testing data, wherein the second testing data are based on the sensor deficiency information; implement the artificial neural network to receive and process the second testing data; and output from the output layer a second testing data output; determine a difference between the first testing data output and the second testing data output; and if the difference is outside of one or more predetermined ranges or does not satisfy one or more predetermined criteria, send a signal representing a result of the determined difference.

An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and / or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Embodiments relate generally to systems and VTOL aerial vehicles. An example central server system comprises: at least one central server system processor; and central server system memory storing central server system program instructions accessible by the at least one central server system processor, and configured to cause the at least one central server system processor to: receive vehicle registration data associated with a manned VTOL aerial vehicle; and determine that the vehicle registration data is associated with a manned VTOL aerial vehicle that is authorized to communicate with the central server system. Some embodiments include VTOL aerial vehicles in communication or communicable with a central server system.

A method of navigating a self-propelled robotic tool (14) within a work area (12) defined by a boundary (13) comprises determining a positioning status comprising a position (P) of the robotic tool (14) within the area; based on the determined position (P) of the robotic tool (14), estimating a boundary distance value indicative of a distance (D1, D2, D3) from the robotic tool (14) to the boundary (13); setting a maximum dead reckoning navigation distance based on the boundary distance value; if a new positioning status cannot be reliably obtained, continuing navigating the robotic tool (14) based on dead reckoning; and if no new positioning status of the robotic tool (14) within the area has been reliably obtained upon reaching the maximum dead reckoning navigation distance, executing a safety operation based on having reached the maximum dead reckoning navigation distance.

A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and / or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and / or generate virtual reconstructions of the vehicle collision.

A system and a method for controlling movement of a robotic vehicle. The robotic vehicle comprises a computer system, a chassis controller, and a plurality of sensors. Data measured by the plurality of sensors is received by the computer system. A base trajectory and an emergency trajectory for the robotic vehicle are generated based on the data. During normal operations of the robotic vehicle the chassis controller causes the robotic vehicle to travel along the base trajectory. After determining that an error has occurred, the chassis controller causes the robotic vehicle to travel along the emergency trajectory.

A system and method includes an autonomous agent having a communication interface that enables the autonomous agent to communicate with a plurality of infrastructure sensing devices; a plurality of distinct health monitors that monitor distinct operational aspects of the autonomous agent; an autonomous state machine that computes a plurality of allowed operating states of the autonomous agent based on inputs from the plurality of distinct health monitors; a plurality of distinct autonomous controllers that generate a plurality of distinct autonomous control instructions; and an arbiter of autonomous control instructions that: collects, as a first input, the plurality of autonomous control instructions generated by each of the plurality of distinct autonomous controllers; collects, as a second input, data relating to the plurality of allowed operating state of the autonomous agent; and selectively enables only a subset of the autonomous control instructions to pass to driving components of the autonomous agent.

An engine-type industrial vehicle includes a power transmission; a main controller; an object detector configured to detect a position of an object; a predicted trajectory calculator configured to derive a predicted trajectory followed by a vehicle body; and a vehicle-speed upper-limit setter configured to impose a vehicle speed limitation on the vehicle body by setting a vehicle-speed upper-limit value when the detected object is positioned on the predicted trajectory and a traveling direction of the vehicle body is a direction toward the object. The main controller is configured to prevent a vehicle speed of the vehicle body from exceeding the vehicle-speed upper-limit value by performing at least one of a control so that a force acts in a direction in which movement of the vehicle body is prevented or a control so that a driving force to the driving wheel is cut off.

A Vehicle Intelligent Unit (VIU) is configured to provide vehicle operations and control for Connected Automated Vehicles (CAV) and, more particularly, to connect with a Collaborative Automated Driving System (CADS) and manage and / or control information exchange between CAV and CADS and manage and / or control CAV lateral and longitudinal movements, including vehicle following, lane changing, and route guidance.

Disclosed is a method of identifying at least one deployed robot in a group of robots, differentially commanding the identified robot, and initiating a remote shutdown or disablement of at least one robot selected from a group of robots.

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver- the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

A system for autonomous or semi-autonomous operation of a vehicle is disclosed. The system includes a machine automation portal (MAP) application configured to enable a computing device to (a) display a map of a work site and (b) provide a graphical user interface that enables a user to (i) define a boundary of an autonomous operating zone on the map and (ii) define a boundary of one or more exclusion zones. The system also includes a robotics processing unit configured to (a) receive the boundary of the autonomous operating zone and the boundary of each exclusion zone from the computing device, (b) generate a planned command path that the vehicle will travel to perform a task within the autonomous operating zone while avoiding each exclusion zone, and (c) control operation of the vehicle so that the vehicle travels the planned command path to perform the task.

Herein, a “perception statistical performance model” (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety / performance testing. A first PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled, in order to compute a first time series of perception outputs. A second time series of perception outputs is computed using a second PSPM for modelling a second perception slice of the runtime stack, the first PSPM learned from data of a first sensor modality of the perception slice and the time series, and the second PSPM learned independently thereof from data of a second sensor modality of the second perception slice and the second time series.

A system for detecting a vehicular collision (i) receives occupant data from at least one internal sensor; (ii) receives external data from at least one external sensor; (iii) determines positional information for at least one occupant of a vehicle; and (iv) generates a virtual reconstruction of the vehicle collision. The virtual reconstruction indicates a severity of vehicle damage and a severity of injury to the at least one occupant caused by the vehicle collision. The system may also utilized vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle systems, and / or perform at least one action to reduce a severity of a potential injury.

Herein, a “perception statistical performance model” (PSPM) for modeling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety / performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modeled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.

This disclosure relates to apparatuses, systems, and methods for handling faults on vehicles. One or more processors in a vehicle may receive, from a control unit in response to a detection of a fault in the vehicle, an indication of a degradation in a performance constraint of the vehicle. The processors may determine, responsive to the degradation in the performance constraint, that the vehicle is unable to execute a set of commands to control a movement of the vehicle along a trajectory. The processors may generate, in accordance with the degradation in the performance constraint, a modified set of commands that the vehicle is able to execute to control the movement of the vehicle along at least a portion of one or more trajectories. The processors may provide the modified set of commands to the control unit of the vehicle to control the movement of the vehicle.

Systems and methods for using risk profiles for fleet management of a fleet of vehicles are disclosed. Fleet management may include determining the performance levels of particular vehicle operators. The risk profiles characterize values representing likelihoods of occurrences of vehicle events. The values are based on vehicle event information for previously detected vehicle events. Exemplary implementations may: receive, from a particular vehicle, particular vehicle event information for particular vehicle events that have been detected by the particular vehicle; determine one or more metrics that quantify a performance level of the particular vehicle operator, based on the risk profiles; compare the one or more metrics for the particular vehicle operator with aggregated metrics that quantify performance levels of a set of vehicle operators; and store, transfer, and / or present results of the comparison.

An aerial vehicle includes a position sensor configured to obtain a present position of the aerial vehicle, a memory configured to store a preset position of the aerial vehicle, and a controller. The controller is configured to calculate, based on the present position and the preset position, safety energy information of the aerial vehicle; and control, based on the safety energy information and a present remaining energy amount of the aerial vehicle, the aerial vehicle to perform a safety protection command.

An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and / or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

A system for flight control configured for use in an electric aircraft includes an inertial measurement unit (IMU) and configured to detect an aircraft angle and an aircraft angle rate. The system includes a flight controller including an outer loop controller configured to receive the input datum from the sensor, receive the aircraft angle from the IMU, and generate a rate setpoint as a function of the input datum. The system includes an inner loop controller configured to receive the aircraft angle rate, receive the rate setpoint from the outer loop controller, and generate a moment datum as a function of the rate setpoint. The system includes a mixer configured to receive the moment datum, perform a torque allocation as a function of the moment datum, and generate a motor command datum as a function of the torque allocation.

The present disclosure generally relates to generating emergency vehicle warnings, automatic control of autonomous vehicles based upon the emergency vehicle warnings. More particularly, the present disclosure relates to generating data representative of emergency vehicle warnings and alternate autonomous vehicle routing based upon real-time information related to an emergency vehicle. The information related to the emergency vehicle may include emergency vehicle origination location data, emergency vehicle current location data, emergency vehicle route data, and / or emergency vehicle destination location data. An emergency vehicle warning and / or alternate vehicle routing for autonomous vehicles may be generated based further on information related to an autonomous vehicle. In one aspect, an emergency vehicle may wirelessly communicate with the autonomous vehicle and / or an insurance provider remote server. The insurance provider may adjust auto insurance for insured individuals, having vehicles with the vehicle safety functionality discussed herein, to reflect lower risk and provide insurance savings to customers.

The embodiments disclose a method including providing an aerial drone coupled wirelessly to a social distancing application on a user digital device, wherein the drone is coupled to solar cell panels for recharging its batteries, providing a strobe light coupled to the drone for signaling an S.O.S. automatically in emergency situations, cellular communication device coupled to the drone for transmitting and receiving messages from the social distancing application, wherein the drone includes a cellular signal strength sensor to automatically move to a location to boost cellular signal strength, and providing at least one camera for capturing images and videos during user directed reconnaissance, drone sensors to detect and measure aerosols including biologics and DNA in an area, electromagnetic fields, barometric pressure, humidity, ambient temperature, wind speed and direction, detection and identification devices to detect unnatural sounds, to analyze and identify manmade, animal and environmental objects and conditions using computer vision.

The invention provides a multi-sensor synchronization control method and device, an autonomous driving system, and a medium. The method includes: determining delay duration of sensors to be synchronized based on coordinates of a preset reference point corresponding to the sensors to be synchronized and control characteristics of the sensors to be synchronized; determining a triggering moment of the sensors to be synchronized based on a preset target sampling moment and the delay duration; and triggering, by using the triggering moment, a synchronous clock signal corresponding to the sensors to be synchronized, so that the sensors to be synchronized sample data on the preset reference point at the target sampling moment. In this way, a different triggering moment can be set for each sensor, and all the sensors can perform sampling on corresponding reference points at the preset target sampling moment, thereby implementing synchronous sampling on the same object by all the sensors, improving the accuracy of a perception result of a perception system, and thus improving the stability and safety of the autonomous driving system.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating an AV includes receiving, by a computer located in the AV, sensor data that indicates a condition of one or more devices in the AV or of a roadway on which the autonomous vehicle is operating; and causing, by the computer and based on the sensor data, the autonomous vehicle to operate in accordance with a maneuver by sending instructions to one or more devices in the autonomous vehicle, wherein the maneuver is configured to bring the autonomous vehicle to a complete stop. In some embodiments, the maneuver may be selected from a plurality of maneuvers in response to determining an occurrence of a fault condition based on the sensor data.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire one or more images from a camera and determine first camera noise values based on the one or more images by determining reactions of camera photo receptors to light. The instructions can include further instructions to compare the first camera noise values with second camera noise values determined based on previously acquired images from the camera and output a tamper determination for the camera based on whether the first camera noise values match, within a tolerance value, the second camera noise values determined based on the previously acquired images from the camera.

A modular vehicle management system is described, comprising a controller module configured to control different types of carrier modules. The controller module includes a computer system and optionally one or more sensors. The computer system is configured to perform operations comprising detecting whether a carrier module is connected to the controller module. If the carrier module is connected to the controller module, the carrier module is authenticated. If the authentication fails, operation of the vehicle is inhibited. The control module is configured to determine carrier module capabilities including information regarding a navigation processing device, and / or a radio modem. The controller adapts to the capabilities of the controller module. Using information from the sensors and the navigation processing device, the vehicle management system navigates the vehicle.

A system for flight control for managing actuators for an electric aircraft is provided. The system includes a controller, wherein the controller is designed and configured to receive a sensor datum from at least a sensor, generate an actuator performance model as a function of the sensor datum, identify a defunct actuator of the electric aircraft as a function of the sensor datum and the actuator performance model, generate an actuator allocation command datum as a function of at least the actuator performance model and at least the identification of the defunct actuator, and perform a torque allocation as a function of the actuator allocation command datum.

An evacuation running assistance system includes a road shoulder evacuation possibility determiner to determine if an own vehicle can be evacuated to a road shoulder; an own vehicle situation determiner to determine a current situation of an own vehicle in accordance with a time limit and the road shoulder evacuation possibility, a controller to control an own vehicle in accordance with the situation of the own vehicle; and a road shoulder evacuation possibility road determiner to acquire evacuation space information from a past running history of the own vehicle. The own vehicle situation determiner determines that the own vehicle is in the situation to be controlled to perform the on-lane stopping when the road shoulder evacuation possibility road determiner does not determine within the provisional time that the evacuation of the own vehicle to the road shoulder is possible.

The UAV 1a includes a dust sensor 16 and a dust-preventing function 17 and performs a processing for a different UAV 1b not provided with a dust-preventing function on the basis of a dust amount detected by the dust sensor 16 during a flight of the UAV 1a.