Abstract: One embodiment of this disclosure can provide a system and method for estimating user gazes. During operation, the system can obtain an event stream captured by an event camera monitoring movement of a user's eye, generate an event image by accumulating, at a pixel level, events that occurred within a predetermined interval based on the event stream and determining a pixel value of each pixel based on the accumulated events associated with the pixel, and input the event image to a gaze-estimation machine learning model, which generates a first prediction output regarding a direction of the user's eye movement and a second prediction output regarding a speed of the user's eye movement.

Abstract: One embodiment of this disclosure can provide a system and method for training a machine learning model to detect pupil centers. During operation, the system can obtain a set of labeled pupil images, with a respective labeled pupil image comprising a pupil-segmentation label and a pupil-center-position label; construct a multitask machine learning model that includes a first branch for performing a pupil-region segmentation task and a second branch for performing a pupil-center-position regression task; and train the multitask machine learning model using the set of labeled pupil images. Training the multitask machine learning model comprises simultaneously training the first and second branches.

Abstract: One embodiment of this disclosure can provide a pair of augmented reality (AR) glasses. The AR glasses can include a pair of lenses and a frame. At least a portion of a lens of the AR glasses can function as a see-through display, and the frame can be embedded with at least a processing unit, a storage unit, a network unit, and a user-interaction unit.

Abstract: Disclosed is a system for automatically navigating a vehicle, which system comprises: a data storage containing one or more maps of one or more areas; electronic interfaces for: (i) receiving data relating to the one or more areas from external data sources; (ii) receiving parameters relating to motion of the vehicle from physical sensors; and (iii) receiving a location of the vehicle from a positioning device; a navigation processor comprising processing circuitry adapted to determine an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem.

Abstract: Disclosed is a system for automatically navigating a vehicle, which system comprises: a data storage containing one or more maps of one or more areas; electronic interfaces for: (i) receiving data relating to the one or more areas from external data sources; (ii) receiving parameters relating to motion of the vehicle from physical sensors; and (iii) receiving a location of the vehicle from a positioning device; a navigation processor comprising processing circuitry adapted to determine an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem.

Abstract: Disclosed is a system for automatically navigating a vehicle, which system comprises: a data storage containing one or more maps of one or more areas; electronic interfaces for: (i) receiving data relating to the one or more areas from external data sources; (ii) receiving parameters relating to motion of the vehicle from physical sensors; and (iii) receiving a location of the vehicle from a positioning device; a navigation processor comprising processing circuitry adapted to determine an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem.