An automated image processing method for assessing facially-expressed emotions of an individual, the facially-expressed emotions being caused by operation of a vehicle, machinery, or robot by the individual, including operating a vehicle, machinery, or robot by the individual and thereby expose a…
A method is provided for the efficient training of a neural network for an Automated Driving System (ADS) of a vehicle, utilizing a Central Processing Unit (CPU) to handle input/output (I/O) operations for reading and writing sensor data from a disk, and a Graphics Processing Unit (GPU) for…
A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the…
An example operation may include one or more of training at least one of an AI model using a neural network training capability with at least one of charging data of energy sources, vehicle location data, and energy availability data, to determine an amount of energy to be stored, determining…
A system, apparatus, computer program product, and method use a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which…
In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors…
Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period…
In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors…
An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a…
In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an…
Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to…
Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images…
The vehicle control method includes: acquiring vehicle parameters of a target vehicle and scene parameters of a scene where the target vehicle is located; searching for a target strategy corresponding to the vehicle parameters and the scene parameters in a strategy library, if the vehicle…
Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period…
Provided are systems, methods, and computer readable media for predicting a vessel rendezvous, and systems, methods, and computer readable media for generating a vessel rendezvous prediction model. The method can include generating or receiving a rendezvous a rendezvous prediction model; receiving…
Herein, a “perception statistical performance model” (PSPM) for modelling 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; determine from the…
In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors…
Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to…
A method, apparatus and computer program product are provided for surveillance of road environments via deep learning. In this regard, one or more features for vehicle sensor data associated with one or more vehicles traveling along a road segment proximate to a vehicle are determined. The vehicle…
System, methods, and other embodiments described herein relate to improving trajectory forecasting in a device. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying an object from the sensor data that is present in the…
Methods and systems for predicting behavior of a dynamic object of interest in an environment of a vehicle are described. Time series feature data are received, representing features of objects in the environment, including a dynamic object of interest. The feature data are categorized into one of…
In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an…
A system, computer program product and a method to predict an objective function based on a recipe, and generate, via a machine learning model, a plurality of proposed different recipes of battery materials for optimizing at least objective function. Instances of the different proposed recipes of…
An embodiment of the present invention provides an artificial intelligence apparatus for controlling an auto stop function, including: an input unit configured to receive brake information and velocity information of a vehicle; a storage unit configured to store a control model for the auto stop…
Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to…
Systems, methods and apparatuses of processing overwhelming stimuli in vehicle data recorders. For example, a data recorder can have resources, such as memory components, a controller, an inference engine, etc. The resources can be partitioned into a first subset and a second subset. Abnormal…
In some examples, a dynamic system, including a vehicle, may be represented using a graph-based representation. One or more nodes in the graph-based representation may correspond to one or more agents in the dynamic system, and one or more edges between the nodes in the graph-based representation…
In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss…
A big data-based driving information provision system may include a sensor configured to measure and collect state monitoring data of an engine, vehicle monitoring data, and vibration data; an engine electronic control unit (ECU) configured to generate a combustion characteristic index (CCI) data…
A method for training a machine learning model for determining a quality grade of data sets from each of a plurality of sensors. The sensors are configured to generate surroundings representations. The method includes: providing data sets of each of the sensors from corresponding surroundings…