Training and utilizing a machine-learning model to automatically predict an identity of a driver based on seat position

Abstract

The described methods and systems enable automatic detection of a driver's identity based on an analysis of position or orientation data pertaining to elements of a cockpit that the driver tends to personalize when driving according to her preferences. Example position or orientation data may include data pertaining to seat or mirror position or orientation. Some of the disclosed embodiments utilize machine-learning techniques to train a machine-learning (ML) model to automatically detect or predict a driver's identity based on learned patterns (e.g., based on preferred positions or orientations the ML model has learned for the driver.) If desired, one or more embodiments may implement unsupervised learning techniques, supervised learning techniques, or both unsupervised and supervised learning techniques.