116results about "Control safety arrangements" patented technology

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 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.

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.

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.

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.

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.

In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, a machine learning model is used to process time series data. In some embodiments, the machine learning model is trained in an unsupervised manner on a large amount of prior time series data, allowing a highly accurate model to be created from new data. In some embodiments, the training of the machine learning model alternates between a fit optimization and a prune optimization to allow a large amount of training data to be processed that includes unlabeled anomaly records. Because a machine learning model is used, anomalies within complex systems can be detected, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When an anomaly is detected, a command can be sent to the monitored system (e.g., autonomous vehicle) to address the anomaly.

A vehicle control unit, including: a first control unit (2-1) with a microcomputer (15) that detects a state of an actuating switch (16) of an electrical component (3), monitors a signal indicating a state of an ignition key and controls the electrical component (3); and characterized by a second control unit (2-2) which is connected to the first control unit (2-1) via a vehicle-internal network (1a, 1b), where the microcomputer (15) when the actuating switch (16) of the electrical component (3) in the first control unit (2-1) is determined to be in an ON state for a longer period of time and the ignition key is determined to be in an OFF state, data transmission from the first control unit (2-1) to the second control unit (2-2) is interrupted, or the first control unit (2-1) is put into a receive-only mode, a driver of a vehicle is notified of a fault and performs a process to detect the cancellation of a longer-term on-state of the actuating switch (16), if the actuating switch (16) of the electrical component (3) is determined to be in the on-state for a longer period of time and the ignition key is determined to be in the on-state, and stores an error report in a memory device and interrupts the notification of the driver if the long-term on state of the actuating switch (16) persists continuously for a certain number of times or continuously for a certain period of time when the ignition key is in the off state after the process for detecting the cancellation of the long-term on state of the actuating switch (16).

An improved distributed information sharing system (DISS) and methods for an autonomous vehicle, the DISS programmed and configured to receive information from a plurality of distributed sensors; determine the existence of an incident, vehicles, passengers, pedestrians, animals and objects involved in the incident, a nature of the injuries and damages from the incident; determine if the autonomous vehicle can be safely moved autonomously from a location where the incident occurred to a second location; contact an emergency responder when the vehicle cannot be safely moved autonomously; receive a request to transfer control of the vehicle from an emergency responder user device; and in response, transfer control of the automated vehicle to a trusted emergency responder without requiring approval from an owner of the vehicle using encryption and handshake techniques; and notify an owner or interested party of the vehicle of the incident.

An aerial vehicle includes a battery, a propulsion system including a motor and a rotor, and a flight controller configured to: in response to one or more parameters of the battery not satisfying a preset condition, cause the aerial vehicle to enter a warm up mode, in which the motor drives the rotor to rotate but the aerial vehicle is prevented from taking off, and while the aerial vehicle is in the warm up mode, in response to the one or more parameters of the battery satisfying the preset condition, allow the aerial vehicle to take off.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

A moving body control method controls a moving body having a function of operating in accordance with a remote instruction in a predetermined area. The moving body control method includes: a remote instruction verification process that determines whether or not the remote instruction received by the moving body is valid; and an operation limiting process that limits at least a part of an operation of the moving body without following the remote instruction, when the remote instruction received by the moving body is invalid. The remote instruction verification process includes: determining whether or not the moving body is present in the predetermined area at a time when the moving body receives the remote instruction; and determining that the remote instruction is invalid, when the moving body is not present in the predetermined area at the time when the moving body receives the remote instruction.

Systems and techniques for reliable real-time deployment of robot safety updates are described herein. Condition data may be collected for an environment in which a robot is operating. The robot may be classified with a condition type. Condition type data selected from the condition data may be analyzed based on the condition type to calculate a safety risk level for the robot. A microservice may be identified to provide a robot safety rule for the robot based on the safety risk level. The microservice may be identified using a safety prediction model generated from the condition type data.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

The application provides a multi-sensor synchronous control method and device, an unmanned system and a medium, comprising: determining a delay duration of a to-be-synchronized sensor based on coordinates of a preset reference point corresponding to the to-be-synchronized sensor and control characteristics of the to-be-synchronized sensor; determining a trigger time of the to-be-synchronized sensor based on a preset target sampling time and the delay duration; and triggering a synchronization clock signal corresponding to the to-be-synchronized sensor by using the trigger time, so that the to-be-synchronized sensor samples data of the preset reference point at the target sampling time. In this way, different trigger times can be set for each sensor, and all sensors can sample corresponding reference points at the preset target sampling time, realizing synchronous sampling of all sensors on the same object, improving the accuracy of the sensing result of the sensing system, and further improving the stability and safety of the unmanned system.

Method (200) for setting a control signal (135) for controlling at least one safety device (130) for a vehicle (100), wherein the method (200) comprises the following steps: Reading (210) a driving mode signal (115) which has a first value when a manual driving mode of the vehicle (100) is set, and which has a second value when at least a partially automatic driving mode of the vehicle (100) is set;and adjusting (220) at least one parameter of the control signal (135) for controlling the at least one safety device (130) using the driving mode signal (115) to set the control signal (135), characterized in that, when responding to the first value of the driving mode signal (115) read in the reading step (210), the at least one parameter is set to a first parameter value in the adjusting step (220), wherein the first parameter value causes a partial triggering of the at least one safety device (130) when the at least one safety device (130) is controlled.

An autonomous vehicle safety system, a method of operating the same and an autonomous vehicle equipped with the autonomous vehicle safety system. An autonomous vehicle Bluetooth receiver is connected in a signal transmitting fashion to the engine control unit of an autonomous vehicle. Upon receiving a signal from a remote Bluetooth transmitter at a predefined proximity distance between the transmitter and the receiver, a microcontroller connected to the receiver actuates a relay switch disabling the engine control unit. The engine control unit may then also engage the vehicle breaks, lock the steering in place, and only after not receiving a proximal Bluetooth signal for a particular time span, enable again the autonomous vehicle operating mode.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for an unmanned aerial system inspection system. One of the methods is performed by a UAV and includes obtaining, from a user device, flight operation information describing an inspection of a vertical structure to be performed, the flight operation information including locations of one or more safe locations for vertical inspection. A location of the UAV is determined to correspond to a first safe location for vertical inspection. A first inspection of the structure is performed is performed at the first safe location, the first inspection including activating cameras. A second safe location is traveled to, and a second inspection of the structure is performed. Information associated with the inspection is provided to the user device.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

The embodiments disclose an aerial drone navigation system including an aerial drone having a camera, LIDAR detection device, and autonomous navigation sensors, a computer vision system onboard the drone identifies human or animal targets using artificial intelligence, a mobile device wirelessly coupled to the drone receives live video, LIDAR data, and tracking information, communication module on the drone includes cellular and mesh hardware for transmitting sensor and location data to the mobile device or remote command nodes, a guidance control module autonomously directs the drone along a calculated path while avoiding static or dynamic obstacles detected by the LIDAR, a command interface receives mission parameters or rerouting instructions from an operator device, a wearable visual display device operable without a physical wired connection, configured to receive augmented or mixed reality overlays, interface with an AI engine for interpreting mission data, and accept voice input for authentication and control.

Systems, devices, and methods for a safety system including: selecting an unmanned aerial vehicle (UAV) command on a controller, the controller comprising a first processor with addressable memory; presenting a first activator and a second activator on a display of the controller for the selected UAV command, wherein the second activator is a slider; and sending the UAV command to a UAV if the first activator and the second activator are selected, the UAV comprising a second processor with addressable memory.

A system for controlling an autonomous vehicle when the autonomous vehicle is outside of its operational design domain. The system includes an environment detection system, a vehicle control system, and a first electronic processor. The first electronic processor is configured to detect that the autonomous vehicle is outside of its operational design domain and send a first electronic message. The first electronic message requests a surrounding vehicle to lead the autonomous vehicle until the autonomous vehicle returns to its operational design domain or reaches a predetermined location. The electronic processor is further configured to determine a lead vehicle and control the autonomous vehicle to follow the lead vehicle until the autonomous vehicle returns to its operational design domain or reaches the predetermined location.

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.

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.