Abstract: Vision-based airbag enablement may include capturing two-dimensional images of a passenger, segmenting the image, classifying the image, and determining seated height of the passenger from the image. Enabling or disabling deployment of the airbag may be controlled based at least in part upon the determined seated height.

Abstract: A vehicle, system and a computer-implemented method for setting a height of a seat belt in a vehicle. The system includes a computer vision module, a motor and a controller. The computer vision module determines a seatbelt-neck distance (SND) and a seatbelt-shoulder distance (SSD) for an occupant of the vehicle. The motor adjusts the height of the seat belt. The controller control the motor to set the height of the seat belt based on the SND and the SSD.

Abstract: Vision-based airbag enablement may include capturing two-dimensional images of a passenger, segmenting the image, classifying the image, and determining seated height of the passenger from the image. Enabling or disabling deployment of the airbag may be controlled based at least in part upon the determined seated height.

Abstract: A system for detecting seatbelt routing includes: a state module configured to, based on a series of images over a period of time from a camera of a vehicle facing a seat, determine and output a routing state of a seatbelt of the seat; and a remediation module configured to, based on the routing state of the seatbelt of the seat, selectively perform at least one remedial action.

Abstract: A seat belt status determining system and method for a vehicle that evaluate a seat belt and determine if it is buckled and if it is properly routed on a passenger. In order to evaluate the seat belt status, the system and method use seat belt sensors and cameras inside the vehicle cabin to generate data that is then analyzed in conjunction with machine learning techniques (e.g., supervised machine learning techniques implemented through the use of neural networks) to classify the seat belt status into one or more categories, such as: passenger not present, passenger present/seat belt not being worn, passenger present/seat belt being worn improperly, passenger present/seat belt being worn properly, and blocked view.

Abstract: A system for detecting seatbelt routing includes: a state module configured to, based on a series of images over a period of time from a camera of a vehicle facing a seat, determine and output a routing state of a seatbelt of the seat; and a remediation module configured to, based on the routing state of the seatbelt of the seat, selectively perform at least one remedial action.

Abstract: A vehicle, system and a computer-implemented method for setting a height of a seat belt in a vehicle. The system includes a computer vision module, a motor and a controller. The computer vision module determines a seatbelt-neck distance (SND) and a seatbelt-shoulder distance (SSD) for an occupant of the vehicle. The motor adjusts the height of the seat belt. The controller control the motor to set the height of the seat belt based on the SND and the SSD.

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: A seat belt status determining system and method for a vehicle that evaluate a seat belt and determine if it is buckled and if it is properly routed on a passenger. In order to evaluate the seat belt status, the system and method use seat belt sensors and cameras inside the vehicle cabin to generate data that is then analyzed in conjunction with machine learning techniques (e.g., supervised machine learning techniques implemented through the use of neural networks) to classify the seat belt status into one or more categories, such as: passenger not present, passenger present/seat belt not being worn, passenger present/seat belt being worn improperly, passenger present/seat belt being worn properly, and blocked view.