A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.

Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.

Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

Described herein are methods of generating learning data to facilitate de-biasing the labeled location of an object of interest within an image. Methods may include: receiving sensor data, where the sensor data is a first image; determining reference corner locations of an object in the first image using image processing; generating observed corner locations of the object in the first image from the determined reference corner locations; generating a bias transformation based, at least in part, on a difference between the reference corner locations and the observed corner locations of the object in the first image; receiving sensor data from another image sensor of a second image; receiving observed corner locations of an object in the second image from a user; and applying the bias transformation to the observed corner locations of the object in the second image to generate de-biased corners for the object in the second image.

Described herein are technologies relating to computing a likelihood of an operation-influencing event with respect to an autonomous vehicle at a geographic location. The likelihood of the operation-influencing event is computed based upon a prediction of a value that indicates whether, through a causal process, the operation-influencing event is expected to occur. The causal process is identified by means of a model, which relates spatiotemporal factors and the operation-influencing events.

An AI robot for cleaning in consideration of a user's action includes a camera to acquire a first image data for the user, a cleaning unit including a suction unit and a mopping unit, a driving unit configured to drive the AI robot, and a processor to determine the user's action using the first image data, determine a cleaning schedule in consideration of the user's action, and control the cleaning unit and the driving unit based on the determined cleaning schedule.

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Systems, apparatuses and methods may provide for technology that generates a sequence of predictive maps based on a sequence of historical maps and overlays the sequence of predictive maps on one another to obtain a map overlay. The technology may also apply an attenuation factor to the map overlay. In one example, the map overlay includes a grid of cells and each cell includes an occupation probability in accordance with the attenuation factor.

An autonomous driving apparatus for accompanied driving in an environment that includes a companion and an obstacle includes a sensor, processing circuitry, and a driver. The sensor may generate sensor data. The processing circuitry may define a current state of the autonomous driving apparatus based on processing the sensor data to determine respective positions of the companion and the obstacle in the environment and select a first tracking point of a plurality of tracking points at least partially surrounding the position of the companion in the environment based on the current state, a position of each tracking point of the plurality of tracking points in the environment defined by the position of the companion in the environment. The driving apparatus drive mechanism may move the autonomous driving apparatus to the first tracking point to cause the autonomous driving apparatus to accompany the companion in the environment.

Systems and methods for robotic disinfection and sanitation of environments are disclosed herein. The robotic devices disclosed herein may detect and map locations of pathogens within environments. The robotic devices disclosed herein may utilize data from sensor units to sanitize objects using desired methods specified by operators. The robotic devices disclosed herein may sanitize environments comprising people.