Abstract: A helmet and a method and system for controlling a digital visor of a helmet are disclosed herein. The helmet includes a visor screen having a plurality of liquid crystal display (LCD) pixels, with each LCD pixel configured to alter in transparency. The helmet also includes a light sensor configured to detect incident light. The helmet also includes a controller coupled to the visor screen and the light sensor. The controller is configured to alter the transparency of the plurality of LCD pixels based on the incident light. In embodiments, the controller can alter the transparency of the LCD pixels based on the direction and/or intensity of the incident light.

Abstract: A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet also includes an inertial movement unit (IMU) configured to collect helmet motion data of a rider of the vehicle and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle and determine a rider attention state utilizing the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU.

Abstract: A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle, an inertial measurement unit (IMU) configured to collect helmet motion data of the helmet associated with a rider of the vehicle, and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle, determine a gesture in response to the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU, and output on a display of the helmet a status interface related to the vehicle, in response to the gesture.

Abstract: A system of a virtual visor includes one or more sensors configured to receive input data including images, wherein the one or more sensors includes at least a camera utilized in the virtual visor, a processor in communication with the one or more sensors. The processor is programmed to create a training dataset utilizing at least the input data, utilizing the training dataset, create a classification associated with a shadow mask region and a first face region associated with a first face, segment the shadow mask region and the first face region from the training data set, and output a shadow representation via the virtual visor and utilizing the shadow mask region, the first face region, and a second face region associated with a second face.

Abstract: A helmet and a method and system for controlling a digital visor of a helmet are disclosed herein. The helmet includes a visor screen having a plurality of liquid crystal display (LCD) pixels, with each LCD pixel configured to alter in transparency. The helmet also includes a light sensor configured to detect incident light. The helmet also includes a controller coupled to the visor screen and the light sensor. The controller is configured to alter the transparency of the plurality of LCD pixels based on the incident light. In embodiments, the controller can alter the transparency of the LCD pixels based on the direction and/or intensity of the incident light.

Abstract: A virtual visor in a vehicle includes a screen with various regions that can alternate between being transparent and being opaque. A camera captures an image of the driver's face. A processor performs facial recognition or the like based on the captured images, and determines which region of the screen is transitioned from transparent to opaque to block out the sun from shining directly into the driver's eyes while maintaining visibility through the remainder of the screen. Low power monitors can be independently run on the vehicle, asynchronously with the algorithms and image processing that controls which region of the screen to be opaque. The monitors consume less power than operating the virtual visor continuously. Based on trigger conditions as detected by the monitors, the image processing and thus the alternating between opaque and transparent is ceased to save power until the trigger condition is no longer present.

Abstract: A virtual visor system is disclosed that includes a visor having a plurality of independently operable pixels that are selectively operated with a variable opacity/transparency. A camera captures images of the face of a driver or other passenger and, based on the captured images, a controller operates the visor to automatically and selectively darken a limited portion thereof to block the sun or other illumination source from striking the eyes of the driver, while leaving the remainder of the visor transparent. The virtual visor system advantageously eliminates unnecessary obstructions to the driver's view while also blocking distracting light sources, thereby improving the safety of the vehicle.

Abstract: A system and method for generating a tracking state for a device includes synchronizing measurement data from exteroceptive sensors and an inertial measurement unit (IMU). A processing unit is programmed to offset one of the measurement signals by a time offset that minimizes a total error between a change in rotation of the device predicted by the exteroceptive sensor data over a time interval defined by an exteroceptive sensor sampling rate and a change in rotation of the device predicted by the IMU sensor data over the time interval.

Abstract: A visual SLAM system comprises a plurality of keyframes including a keyframe, a current keyframe, and a previous keyframe, a dual dense visual odometry configured to provide a pairwise transformation estimate between two of the plurality of keyframes, a frame generator configured to create keyframe graph, a loop constraint evaluator adds a constraint to the receiving keyframe graph, and a graph optimizer configured to produce a map with trajectory.

Abstract: A helmet includes one or more sensors located in the helmet and configured to obtain cognitive-load data indicating a cognitive load of a rider of a vehicle, a wireless transceiver in communication with the vehicle, a controller in communication with the one or more sensors and the wireless transceiver, wherein the controller is configured to determine a cognitive load of the occupant utilizing at least the cognitive-load data and send a wireless command to the vehicle utilizing the wireless transceiver to execute commands to adjust a driver assistance function when the cognitive load is above a threshold.

Abstract: A smart helmet includes a heads-up display (HUD) configured to output graphical images within a virtual field of view on a visor of the smart helmet. A transceiver is configured to communicate with a mobile device of a user. A processor is programmed to receive, via the transceiver, calibration data from the mobile device that relates to one or more captured images from a camera on the mobile device, and alter the virtual field of view of the HUD based on the calibration data. This allows a user to calibrate his/her HUD of the smart helmet based on images received from the user's mobile device.

Abstract: A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle, an inertial measurement unit (IMU) configured to collect helmet motion data of the helmet associated with a rider of the vehicle, and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle, determine a gesture in response to the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU, and output on a display of the helmet a status interface related to the vehicle, in response to the gesture.

Abstract: A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet also includes an inertial movement unit (IMU) configured to collect helmet motion data of a rider of the vehicle and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle and determine a rider attention state utilizing the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU.

Abstract: A system for providing a rider of a saddle-ride vehicle, such as a motorcycle, with information about helmet usage is provided. A camera is mounted to the saddle-ride vehicle and faces the rider and monitor a rider of the vehicle and collect rider image data. A GPS system is configured to detect a location of the saddle-ride vehicle. A controller is in communication with the camera and the GPS system. The controller is configured to receive an image of the ruder from the camera, determine if the rider is wearing a helmet based on the rider image data, and output a helmet-worn indicator to the rider, in which the helmet-worn indicator varies based on the determined location of the saddle-ride vehicle.

Abstract: A system and method for generating a. tracking state for a device includes synchronizing measurement data from exteroceptive sensors and an inertial measurement unit (IMU). A processing unit is programmed to offset one of the measurement signals by a time offset that minimizes a total error between a change in rotation of the device predicted by the exteroceptive sensor data over a time interval defined by an exteroceptive sensor sampling rate and a change in rotation of the device predicted by the IMU sensor data over the time interval.

Abstract: A method for motion estimation in an augmented reality (AR) system includes receiving inertial sensor data and image data during movement of the AR system generating a probability map based on the inertial sensor data and the image data, the probability map corresponding to one frame in the image data and including probability values indicating that each pixel in the one frame is in an inertial coordinate frame or a local coordinate frame with a convolutional neural network encoder/decoder, identifying visual observations of at least one landmark in the local coordinate frame based on the image data and the probability map, and generating an estimate of secondary motion in the local coordinate frame based on a first prior state in a hidden Markov model (HMM) corresponding to the local coordinate frame and the visual observations of the at least one landmark in the local coordinate frame.

Abstract: A visual SLAM system comprises a plurality of keyframes including a keyframe, a current keyframe, and a previous keyframe, a dual dense visual odometry configured to provide a pairwise transformation estimate between two of the plurality of keyframes, a frame generator configured to create keyframe graph, a loop constraint evaluator adds a constraint to the receiving keyframe graph, and a graph optimizer configured to produce a map with trajectory.

Abstract: A method for motion estimation in an augmented reality (AR) system includes receiving inertial sensor data and image data during movement of the AR system generating a probability map based on the inertial sensor data and the image data, the probability map corresponding to one frame in the image data and including probability values indicating that each pixel in the one frame is in an inertial coordinate frame or a local coordinate frame with a convolutional neural network encoder/decoder, identifying visual observations of at least one landmark in the local coordinate frame based on the image data and the probability map, and generating an estimate of secondary motion in the local coordinate frame based on a first prior state in a hidden Markov model (HMM) corresponding to the local coordinate frame and the visual observations of the at least one landmark in the local coordinate frame.