In an aspect of the disclosure, methods, a computer-readable media, and apparatus are provided. A method for wireless communication includes detecting a first object using one or more sensors. The method includes receiving one or more messages from one or more second devices indicating detection of one or more second objects. The one or more messages indicating information about the one or more second objects. The method further includes selecting information about the first object to report in a message to one or more third devices based on whether the first object corresponds to at least one object of the one or more second objects in the one or more messages.

The disclosure relates to a lane line determination method and system, a vehicle, and a storage medium, and the lane line determination method includes: capturing a road image for a current location in a vehicle coordinate system; recognizing the road image and generating basic lane lines for the current location; extracting map lane lines for the current location; mapping the map lane lines to the vehicle coordinate system to obtain auxiliary lane lines; registering the basic lane lines with the auxiliary lane lines, the registration being performed based on confidence levels of the basic lane lines; and generating target lane lines based on the registered auxiliary lane lines and the basic lane lines. According to the lane line determination method, an operation such as correction can be performed, according to a condition, on a visually captured lane line by using a map lane line, thereby improving accuracy of the lane line detection.

A method and system, the method including transmitting a unique identifier of at least one radio frequency (RF) transmitter to a device in a vicinity of the RF transmitter; acquiring images of objects by a vision system, the vision system comprising at least one image capturing device and an image processing unit to determine objects in the images acquired by the image capturing device; determining, by a controller, a location of the device based on, at least in part, the unique identifier of one of the at least one RF transmitters; and determining, by the controller, a precise location of the device based on a correlation between the location of the device determined based on the unique identifier and the objects captured in the images acquired by the image capturing device.

A mechanism is described for facilitating embedding of human labeler influences in machine learning interfaces in computing environments, according to one embodiment. A method of embodiments, as described herein, includes detecting sensor data via one or more sensors of a computing device, and accessing human labeler data at one or more databases coupled to the computing device. The method may further include evaluating relevance between the sensor data and the human labeler data, where the relevance identifies meaning of the sensor data based on human behavior corresponding to the human labeler data, and associating, based on the relevance, human labeler data with the sensor data to classify the sensor data as labeled data. The method may further include training, based on the labeled data, a machine learning model to extract human influences from the labeled data, and embed one or more of the human influences in one or more environments representing one or more physical scenarios involving one or more humans.

A method of training an image-text retrieval model, a method of multimodal image retrieval, an electronic device and a storage medium, each relating to the technical field of artificial intelligence, and in particular, to fields of computer vision and deep learning technologies. Sample data including a sample text and a sample image is acquired. The sample text includes a sample text in a first language and a sample text in a second language. The sample text in the first language and the sample text in the second language are processed by using the text encoding sub-model to obtain a sample text feature of the sample data. The sample image is processed by using the image encoding sub-model to obtain a sample image feature of the sample data. The image-text retrieval model is trained according to the sample text feature and the sample image feature.

A method for controlling an electronic apparatus includes storing a plurality of artificial intelligence models in a first memory, based on receiving a control signal for loading a first artificial intelligence model among the plurality of stored artificial intelligence models into a second memory, identifying an available memory size of the second memory, and based on a size of the first artificial intelligence model being larger than the available memory size of the second memory, obtaining a first compression artificial intelligence model by compressing the first artificial intelligence model based om the available memory size of the second memory, and loading the first compression artificial intelligence model into the second memory.

A system for cross-modal data retrieval is provided that includes a neural network having a time series encoder and text encoder which are jointly trained using an unsupervised training method which is based on a loss function. The loss function jointly evaluates a similarity of feature vectors of training sets of two different modalities of time series and free-form text comments and a compatibility of the time series and the free-form text comments with a word-overlap-based spectral clustering method configured to compute pseudo labels for the unsupervised training method. The computer processing system further includes a database for storing the training sets with feature vectors extracted from encodings of the training sets. The encodings are obtained by encoding a training set of the time series using the time series encoder and encoding a training set of the free-form text comments using the text encoder.

A computer-implemented method for the safe, active training of a computer-aided model for modeling time series of a physical system using Gaussian processes, including the steps of establishing a safety threshold value α; initializing by implementing safe initial curves as input values on the system, creating an initial regression model and an initial safety model; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section; implementing the determined new curve section on the physical system and measuring output variables; incorporating the new output values in the regression model and the safety model until N passes have been carried out; and outputting the regression model and the safety model.

A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

A vehicle computer comprises a processor and a memory. The memory stores instructions executable by the processor to detect debris flying above a roadway, to input vehicle sensor data to a first classifier that outputs a source of the debris, and based on the source of the debris, to compare sensor data representing the debris to stored reference data to determine a type of physical material included in the debris. The memory stores instruction to input the type of physical material and an environmental condition to a second classifier that outputs a risk assessment, and to actuate the vehicle based on the risk assessment.