Abstract: A robot including a resource sharing circuit, configured to operate according to a resource utilization limit, and comprising a processor, configured to receive resource data representing a resource utilization of the robot; if the resource utilization of the robot is within a predetermined range, operate according to a first operational mode, wherein an upper limit of the predetermined range is defined by a tolerance relative to the resource utilization limit; if the resource utilization of the robot is outside of the predetermined range, operate according to a second operational mode; wherein the first operational mode includes controlling a communication circuit to send a first wireless signal representing an availability to accept a task; and wherein the second operational mode includes controlling the communication circuit to send a second wireless signal representing an availability to allocate a task to an external device for remote processing.

Abstract: Technologies for autonomous vehicle driving quality of service (QoS) determination and communication include an advanced vehicle with a vehicle computing device. The computing device determines multiple route segments of one or more routes to a destination. The computing device determines, for each route segment, one or more autonomous driving factors that are each indicative of an autonomy level achievable by the advanced vehicle for the associated route segment. Factors may include map availability, weather conditions, road conditions, or other factors. The computing device determines, for each route segment, an autonomous QoS score based on the autonomous driving factors. The computing device may rank multiple routes to the destination based on the autonomous QoS scores associated with the route segments of those routes. The computing device may display a proposed route to a user of the advanced vehicle and receive a selection from the user. Other embodiments are described and claimed.

Abstract: An apparatus comprising at least one interface to receive sensor data from a plurality of sensors of a vehicle; and one or more processors to autonomously control driving of the vehicle according to a path plan based on the sensor data; determine that autonomous control of the vehicle should cease; send a handoff request to a remote computing system for the remote computing system to control driving of the vehicle remotely; receive driving instruction data from the remote computing system; and control driving of the vehicle based on instructions included in the driving instruction data.

Abstract: A disclosed example method to predict an injury for a target player on a target date includes determining a first probability of injury of the target player based on probabilities of injuries of second players having similarities with the target player; determining a second probability of injury of the target player based on injuries of the target player; determining a third probability of injury of the target player based on the first probability of injury of the target player and the second probability of injury of the target player; and generating, by executing an instruction with the processor, a report of a predicted probability of injury of the target player for the target date based on the third probability of injury of the target player.

Abstract: Sensor data is received from a plurality of sensors, where the plurality of sensors includes a first set of sensors and a second set of sensors, and at least a portion of the plurality of sensors are coupled to a vehicle. Control of the vehicle is automated based on at least a portion of the sensor data generated by the first set of sensors. Passenger attributes of one or more passengers within the autonomous vehicles are determined from sensor data generated by the second set of sensors. Attributes of the vehicle are modified based on the passenger attributes and the sensor data generated by the first set of sensors.

Abstract: Various systems and methods for detecting risk conditions in a physical workspace. An apparatus can include an interface to receive smart sensor signals from at least one autonomous mobile entity (AME) in the physical workspace. The apparatus can also include processing circuitry coupled to the interface to detect a risk condition associated with the at least one AME, based on the smart sensor signals, relative to a user device associated with a human present in the physical workspace. The processing circuitry can also detect a direction of the risk condition relative to the user device and cause a notification to the first user device. The notification can indicate the direction of the risk condition relative to the user device. Other systems, methods and apparatuses are described.

Abstract: Technologies for performing sensor fusion include a compute device. The compute device includes circuitry configured to obtain detection data indicative of objects detected by each of multiple sensors of a host system. The detection data includes camera detection data indicative of a two or three dimensional image of detected objects and lidar detection data indicative of depths of detected objects. The circuitry is also configured to merge the detection data from the multiple sensors to define final bounding shapes for the objects.

Abstract: Technologies for efficient identity recognition based on skin features include a compute device. The compute device includes an image capture device and an image acquisition module to obtain, with the image capture device, an image that depicts the skin of the person. The compute device also includes a skin feature determination module to identify pixels in the obtained image that are associated with the skin of the person, and determine one or more features of the skin based on the identified pixels. Additionally, the compute device includes an identity determination module to generate a feature vector that includes the determined features of the skin, and analyze the feature vector with reference data to determine an identity of the person. Other embodiments are described and claimed.

Abstract: In one embodiment, a device comprises interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a video stream captured by a camera during performance of an industrial process, wherein the video stream comprises a sequence of frames; detects, based on analyzing the sequence of frames, a degree of particle scatter that occurs during performance of the industrial process; and determines, based on the degree of particle scatter, that an anomaly occurs during performance of the industrial process.

Abstract: Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.

Abstract: Methods, systems and apparatuses may provide for technology that conducts an automated vision analysis of image data associated with an interior of a vehicle cabin, determines a state of a child restraint system (CRS) based on the automated vision analysis, and generates an alert if the state of the CRS does not satisfy one or more safety constraints. In one example, the technology identifies the safety constraint(s) based on a geographic location of the vehicle cabin.

Abstract: A disclosed example method to predict an injury for a target player on a target date includes determining a first probability of injury of the target player based on probabilities of injuries of second players having similarities with the target player; determining a second probability of injury of the target player based on injuries of the target player; determining a third probability of injury of the target player based on the first probability of injury of the target player and the second probability of injury of the target player; and generating, by executing an instruction with the processor, a report of a predicted probability of injury of the target player for the target date based on the third probability of injury of the target player.

Abstract: Methods, systems and apparatuses may provide for technology that conducts an automated vision analysis of image data associated with an interior of a vehicle cabin, determines a state of a child restraint system (CRS) based on the automated vision analysis, and generates an alert if the state of the CRS does not satisfy one or more safety constraints. In one example, the technology identifies the safety constraint(s) based on a geographic location of the vehicle cabin.

Abstract: A device for a vehicle may include a first wireline interface configured to receive a first data stream from a first sensor having a first sensor type for perceiving a surrounding of the vehicle, the first data stream including raw sensor data detected by the first sensor; a second wireline interface configured to receive a second data stream from a second sensor having a second sensor type for perceiving the surrounding of the vehicle, the second data stream including raw sensor data detected by the second sensor; one or more processors configured to generate a coded packet including the received first data stream and the received second data stream by employing vector packet coding on the first data stream and the second data stream; and an output wireline interface configured to transmit the generated coded packet to one or more target units of the vehicle.

Abstract: In one embodiment, a processing device for training a decision tree model includes memory and processing circuitry. The processing circuitry allocates a tree node array in memory, where the number of array elements in the tree node array equals the number of data samples in a training dataset. The processing circuitry also obtains the training dataset, which contains data samples captured at least partially by sensor(s). The processing circuitry then trains the decision tree model. For example, a root node is initially assigned to the data samples in the training dataset. The root node is recursively split into child nodes based on identified branch conditions, where each child node is assigned to a subset of data samples. The tree node array is continuously updated during training to identify the child nodes assigned to the data samples. The processing circuitry then stores the trained decision tree model in memory.

Abstract: Apparatuses, methods and storage medium associated with monitoring or assisting in monitoring of an equipment process are disclosed herein. In embodiments, an apparatus may comprise an analyzer to: receive a plurality of simulation results of a plurality of control limit and alert zone combinations for potential use with a control chart to monitor the equipment process, and calculate a plurality of performance metrics for each of the plurality of control limit and alert zone combinations, using the plurality of simulation results. The apparatus may further select an optimal combination of control limits and alert zones, based at least in part on the plurality of performance metrics, and configure an equipment process monitor with the selected optimal combination of control limits and alert zones for use with a control chart to monitor the equipment process. Other embodiments may be described or claimed.

Abstract: Systems, apparatuses and methods may provide for technology that assigns confidence levels to data bins containing similarity data and length of stay data, wherein the similarity data and the length of stay data correspond to a plurality of previous admissions. Additionally, the confidence levels may be weighted based on a distribution metric that assigns higher weights to denser regions. A length of stay of a target admission may be predicted based on the weighted confidence levels.

Abstract: Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.

Abstract: In one embodiment, an edge computing device for performing decision tree training and inference includes interface circuitry and processing circuitry. The interface circuitry receives training data and inference data that is captured, at least partially, by sensor(s). The training data corresponds to a plurality of labeled instances of a feature set, and the inference data corresponds to an unlabeled instance of the feature set. The processing circuitry: computes a set of feature value checkpoints that indicate, for each feature of the feature set, a subset of potential feature values to be evaluated for splitting tree nodes of a decision tree model; trains the decision tree model based on the training data and the set of feature value checkpoints; and performs inference using the decision tree model to predict a target variable for the unlabeled instance of the feature set.

Abstract: Methods, apparatus, systems, and articles of manufacture for monitoring robot health in manufacturing environments are described herein. An example system, to monitor health of a robot in a semiconductor wafer manufacturing facility, includes a sensor coupled to the robot. The sensor is to obtain a vibration signal representative of vibration of the robot. The example system also includes a health monitor extract a feature from the vibration signal, compare the feature to a threshold, and, in response to determining the feature satisfies the threshold, transmit an alert.