Veoneer US, Inc.

The Charles Stark Draper Laboratory, Inc.

Afrah Faiz Naik

Jon Dee Demerly

Caroline Chung

Scott Lennox

Troy B Jones

Mitchell Pleune

Kyra Horne

Stephen Sheldon Strong

Matthew Graham

A method for detecting seatbelt positioning comprises: capturing, by a camera, a source image including a plurality of pixels each having a corresponding brightness level; determining a range of brightness levels of a group of the plurality of pixels located within at least one region of the source image; generating an adjusted image by adjusting the corresponding brightness levels of the group of the pixels located within the region of the source image based on the range of the brightness levels of the group of the plurality of pixels located within the at least one region of the source image; converting the adjusted image to a black-and-white image; and scanning across the black-and-white image to detect a plurality of transitions between black and white segments corresponding to a predetermined pattern of the seatbelt, and using detections of the plurality of transitions to indicate a detection of the seatbelt.

Method and system for seatbelt detection using adaptive histogram normalization

Previous self-driving car systems can detect objects separately with either vision systems, RADAR systems or LIDAR systems. In an embodiment of the present invention, an object fusion module normalizes sensor output from vision, RADAR, and LIDAR systems into a common format. Then, the system fuses the object-level sensor data across all systems by associating all objects detected and predicting tracks for all objects. The present system improves over previous systems by using the data from all sensors combined to develop a single set of knowledge about the objects around the self-driving car, instead of each sensor operating separately.

Autonomous vehicle: object-level fusion

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Waltham, MA, United States of America

Afrah Faiz Naik