Disclosed are systems, apparatuses, and methods to determine, communicate, and/or respond to state information of at least one of a suspended load control apparatus, carrier, or load suspended by a cable from the carrier, wherein response to the state information may be to control at least one of the suspended load control apparatus, carrier, or load suspended by a cable from the carrier.

Disclosed are systems, apparatuses, and methods to determine, communicate, and/or respond to state information of at least one of a suspended load control apparatus, carrier, or load suspended by a cable from the carrier, wherein response to the state information may be to control at least one of the suspended load control apparatus, carrier, or load suspended by a cable from the carrier.

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

An obstacle detection method can improve the accuracy of determining a relative positional relationship between two or more obstacles that are obstructed or obscured during automated driving. A road scene image of a road where a target vehicle is located is acquired. Obstacle recognition is performed to obtain region information and depth-of-field information corresponding to each obstacle in the road scene image. Target obstacles in an occlusion relationship and a relative depth-of-field relationship between the target obstacles are determined. A ranging result of each obstacle is acquired using a ranging apparatus corresponding to the target vehicle. An obstacle detection result of the road is determined based on the relative depth of field relationship between the target obstacles and the ranging result of each obstacle, thereby improving the accuracy of determining a positional relationship of obstructed or obscured obstacles during automated driving.

Systems and methods for tracking objects though a traffic control system include a plurality of sensors configured to capture data associated with a traffic location, and a logic device configured to detect one or more objects in the captured data, determine an object location within the captured data, transform each object location to world coordinates associated with one of the plurality of sensors; and track each object location using the world coordinates using prediction and occlusion-based processes. The plurality of sensors may include a visual image sensor, a thermal image sensor, a radar sensor, and/or another sensor. An object localization process includes a trained deep learning process configured to receive captured data from one of the sensors and determine a bounding box surrounding the detected object and output a classification of the detected object. The tracked objects are further transformed to three-dimensional objects in the world coordinates.

A modeling method and an apparatus are disclosed. The method includes: obtaining a first data set of a first indicator, and determining, based on the first data set, a second indicator similar to the first indicator; and determining a first model based on one or more second models associated with the second indicator. The first model is used to detect a status of the first indicator, and the status of the first indicator includes an abnormal state or a normal state. The second models are used to detect a status of the second indicator, and the status of the second indicator includes an abnormal state or a normal state.

Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with the host vehicle and identify a representation of a target object in the first output. The processing device may determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output and, if the at least one navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

A method evaluates a degree of fatigue of a vehicle occupant in a vehicle. A number of first fatigue indicators is provided which are determined according to computation rules from a plurality of first sensor values and each represent a degree of fatigue of the vehicle occupant. The first sensor values represent measured values of the vehicle and/or measured values relating to a current journey. A first metadata record is associated with each of the number of first fatigue indicators, wherein the first metadata records represent information about the characteristics of the sensors. The first sensor values are processed in the respective first fatigue indicators. A number of second fatigue indicators is provided which are determined according to computation rules from one or more second sensor values and each represent a degree of fatigue of the vehicle occupant. The second sensor values represent physiological and/or physical parameters of the vehicle occupants. A second metadata record is associated with each of the number of second fatigue indicators. The second metadata records represent information about the characteristics of the sensors. The second sensor values are processed in the respective second fatigue indicators. An overall fatigue indicator is determined which represents the degree of fatigue of the vehicle occupant by weighting the number of first fatigue indicators and the number of second fatigue indicators. The fatigue indicators are weighted according to the information about the characteristics of the sensors contained in the first metadata record and the second metadata record.

This disclosure relates generally to a unified platform for domain adaptable human behaviour inference. The platform provides a unified, low level inference and high level inference of domain adaptable human behaviour inference. The low level inferences include cross-sectional analysis techniques to infer location, activity, physiology. Further the high inference that provide useful and actionable for longitudinal tracking, prediction and anomaly detection is performed based on several longitudinal analysis techniques that include welch analysis, cross-spectrum analysis, Feature of interest (FOI) identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with low level inference, human meta-data and application domain knowledge. Further the unified human behaviour inference can be obtained across multiple domains that include health, retail and transportation.