Described herein are assistant robots that anticipate needs of one or more people (or animals). The assistant robots may recognize a current activity, knowledge of the person's routines, and contextual information. As such, the assistant robots can provide or offer to provide appropriate robotic assistance. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.

A system and method for reliably determining lanes of a roadway includes an optical sensing arrangement for sensing metallic striping from photoelectric effect. The location of the striping that defines a border of a traffic lane is determined and the location of the striping is displayed on a graphical user interface. The location can be used to provide lane control to ensure the vehicle maintains proper position in a traffic lane, lane warning assistance, collision avoidance, parking control, and guidance for autonomous driving.

A target recognition method includes performing first image processing on first image data, to obtain first artificial intelligence (AI) input data; performing second image processing on second image data, to obtain second AI input data, where exposure duration corresponding to the first AI input data is different from exposure duration corresponding to the second AI input data, or a dynamic range corresponding to the first AI input data is different from a dynamic range corresponding to the second AI input data, and both the first image data and the second image data are raw image data generated by an image sensor; and performing target recognition based on the first AI input data and the second AI input data, and determining target information.

Apparatuses, systems, and techniques to indicate an extent, to which text corresponds to one or more images. In at least one embodiment, an extent to which text corresponds to one or more images is indicated using one or more neural networks and used to train the one or more neural networks.

A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

A vehicular driver assistance system includes a front camera module (FCM) disposed at a vehicle. The system, responsive to processing captured image data, generates FCM lane information including information regarding a traffic lane the vehicle is currently traveling along. An e-Horizon module (EHM) generates EHM lane information including information regarding the traffic lane the vehicle is currently traveling along. The vehicular driver assistance system determines an FCM correlation using the FCM lane information and sensor data captured by at least one exterior sensor. The vehicular driver assistance system determines an EHM correlation using the EHM lane information and the sensor data captured by the at least one exterior sensor. Responsive to determining the FCM correlation and the EHM correlation, the system controls lateral movement of the vehicle based on one selected from the group consisting of (i) the FCM lane information and (ii) the EHM lane information.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

The embodiments herein disclose a system and a method for providing an online web-based interactive audio-visual platform for note creation, presentation, sharing, organizing, and analysis. The system provides a conceptual and interactive interface to content; analyses a student's notes and instantly determines the accuracy of the conceptual connections made and a student's understanding of a topic. The system enables the student to add and use audio, visual, drawing, text notes, and mathematical equations in addition to those suggested by the note taking solution; to collate notes from various sources in a meaningful manner by grouping concepts using colors, images, and text; and to personalize other maps developed within the same environment while maintaining links back to the original source from which the notes are derived. The system highlights keywords in conjunction with spoken text to complement the advantages of using visual maps to improve learning outcomes.

Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

The subject disclosure is directed towards a framework that is configured to allow different background-foreground segmentation modalities to contribute towards segmentation. In one aspect, pixels are processed based upon RGB background separation, chroma keying, IR background separation, current depth versus background depth and current depth versus threshold background depth modalities. Each modality may contribute as a factor that the framework combines to determine a probability as to whether a pixel is foreground or background. The probabilities are fed into a global segmentation framework to obtain a segmented image.