Human-vehicle interaction habit learning method and system based on breadth

Abstract

The invention discloses a human-vehicle interaction habit learning method and system based on a breadth learning algorithm. Through the method provided by the invention, the learning is performed through an increment learning model according to vehicle using habit data of a user and real-time data of the user, and prediction learning content corresponding to the vehicle using habits of the user can be predicted, so that the user is unnecessary to frequently control the vehicle, but the vehicle directly executes the control according to the learning content, the interaction frequency between the vehicle and the user is reduced, and the user experience is improved.

Description

Technical field [0001] This application relates to the field of vehicle control technology, and in particular to a method and system for learning human-vehicle interaction habits of a width learning algorithm. Background technique [0002] With the rapid increase in car ownership, the per capita travel mileage has also increased significantly, traffic conditions have become more and more complicated, and drivers have gradually become dependent on various dedicated in-vehicle devices and smartphone-based APP applications. Various car-assisted driving equipment, dedicated car navigation equipment, and smartphone-based map APP applications are also extremely active in the automotive front-end and rear-end markets. [0003] In addition to the conventional APP and other applications, the on-board voice system has also been widely used. For example, users can control the music and air conditioning in the car through voice, or control other functions. In this way, users can more convenien...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a human-vehicle interaction habit learning method and system based on a breadth learning algorithm

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