Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.

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.

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.

A method for evaluating contextual risk profiles at a computing device (110, 212, 312) in a vehicle (310), the method including obtaining information about a proximate vehicle (320, 330); utilizing the information to create a risk profile for the proximate vehicle (320, 330); and based on the risk profile, initiating an action at computing device (110, 212, 312).

The present disclosure relates to a self-propelled robotic tool (1) and a method in a self-propelled robotic tool (1), being used to detecting lifting of the self-propelled robotic device from the ground. The method includes collecting (21) driving data (31) related to the driving of a wheel (5), collecting (23) measured inertia data from an inertial measurement unit (13), IMU, in the self-propelled robotic tool, determining (25), using an estimation function (33), a residual parameter corresponding to a differential between said measured inertia data and estimated inertia data resulting from said driving data being input to said estimation function, and determining a lifting condition based on the residual parameter.

The present specification provides systems, devices and methods for controlling an uncrewed aerial vehicle (UAV) at a public safety incident. An example method contemplates placing a UAV in a shadow mode that follows a firefighter’s movements throughout the PSI while monitoring voice activity while the UAV performs tasks such as sending images from a camera to a central server. Potential voice commands are extracted from the voice activity and associated with tasks being performed by the UAV. A machine learning dataset is built from those associations such that at future incidents the UAV can operate in a freelance mode based on detected voice commands or other contextual factors.

In various examples, permanent faults in hardware component(s) and/or connections to the hardware component(s) of a computing platform may be predicted before they occur using in-system testing. As a result of this prediction, one or more remedial actions may be determined to enhance the safety of the computing platform (e.g., an autonomous vehicle). A degradation rate of a performance characteristic associated with the hardware component may be determined, detected, and/or computed by monitoring values of performance characteristics over time using fault testing.

An object of the present invention is to enhance the reliability of an autonomous driving system. The autonomous driving system includes: a higher-level control device 1 that outputs a control target value of an actuator group based on an action plan of a vehicle; and a lower-level control device 2 that controls the actuator group of the vehicle based on a command from the higher-level control device 1. The lower-level control device 2 holds the control target value of the vehicle provided by the higher-level control device 1 over a specific period. When the higher-level control device 1 does not satisfy a desired function, the lower-level control device 2 is configured to be controlled based on the held control target value. The action plan is followed by determining and correcting a difference between an actual action value and the control target value of the vehicle.

A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.

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.