Abstract:
A vehicle, system and method of adjusting a mirror of a vehicle. A system for adjusting a mirror of a vehicle is disclosed. The system includes a calibration a calibration marker disposed on the vehicle, a camera, a motor and a processor. The calibration marker forms a calibration image onto a face of an occupant of the vehicle via reflection through the mirror. The camera obtains a camera image including the calibration image and the face of the occupant. The processor determines from the camera image an initial location of the calibration image at the face, determines a calibrated setting of the mirror that places the calibration image at a calibration location, and operates the motor to adjust the mirror to the calibrated setting.
Abstract:
Methods and systems are provided for adapting a speech system. In one example a method includes: processing a spoken command with one or more models of one or more model types to achieve model results; evaluating a frequency of the model results; and selectively updating the one or more models of the one or more model types based on the evaluating.
Abstract:
Methods and systems are provided for adapting a speech system. In one example a method includes: logging speech data from the speech system; processing the speech data for a pattern of a user competence associated with at least one of task requests and interaction behavior; and selectively updating at least one of a system prompt and an interaction sequence based on the user competence.
Abstract:
A system for vehicle trajectory planning. The system may include a polarized camera system configured to generate polarized images of a roadway to be driven over with a vehicle, a prior controller configured to generate a prior for the roadway based at least in part on the polarized images, and a driving assistance system configured to provide a driving assistance according to the prior.
Abstract:
A system and method for estimation of a driver state based on eye gaze includes, capturing and sending, using an outward looking camera situated in a vehicle, a first video stream of surrounding environment to a neural controller. The neural controller, based on the first video stream, generates an expected gaze distribution. Using an inward looking camera situated in the vehicle, the camera captures and sends a second video stream of a face of a driver to an eye tracker controller, where based on the second video stream, the eye tracker controller extracts a plurality of gaze directions. A gaze distribution module generates, based on the plurality of gaze directions, an actual gaze distribution. A distance distribution controller, based on a difference between the expected gaze distribution and the actual gaze distribution, generates a distance measure where a determination is made that the distance measure exceeds a threshold.
Abstract:
A system and method for error estimation in an eye gaze tracking system in a vehicle may include an operator monitoring system providing measured eye gaze information corresponding to an object outside the vehicle and an external object monitoring system providing theoretical eye gaze information and an error in the measured eye gaze information based upon the measured eye gaze information and the theoretical eye gaze information.
Abstract:
A method and system can control a speech recognition system in a vehicle. The method includes monitoring adaptive feature data about interactions between a user and the speech recognition system. The method includes determining a first group of samples of the adaptive feature data and creating a control chart based on the first group of samples. The control chart includes a control limit. The method further includes determining a second group of samples of the adaptive feature data after creating the control chart. Furthermore, the method includes calculating an arithmetic mean of each sample of the second group of samples to determine a sample mean, comparing the sample mean to the control limit in order identify unexpected performance of the speech recognition system. The method includes adjusting the speech recognition system based on the identified unexpected performance if the unexpected performance is identified.
Abstract:
A method of training a disparity estimation network. The method includes obtaining an eye-gaze dataset having first images with at least one gaze direction associated with each of the first images. A gaze prediction neural network is trained based on the eye-gaze dataset to develop a model trained to provide a gaze prediction for an external image. A depth database is obtained that includes second images having depth information associated with each of the second images. A disparity estimation neural network for object detection is trained based on an output from the gaze prediction neural network and an output from the depth database.
Abstract:
An occupant monitoring apparatus is provided. The apparatus includes a laser illuminator configured to emit a laser to illuminate an occupant; and a sensor configured to generate an image of the occupant illuminated by the laser.
Abstract:
Methods and systems are provided for recovering from an error in a speech recognition system. In one embodiment, a method includes: receiving, by a processor, a first command recognized from a first speech utterance by a first language model; receiving, by the processor, a second command recognized from the first speech utterance by a second language model; determining, by the processor, at least one of similarities and dissimilarities between the first command and the second command; processing, by the processor, the first command and the second command with at least one rule of an error model based on the similarities and dissimilarities to determine a root cause; and selectively executing a recovery process based on the root cause.