User classification method based on adas (advanced driver assistant system) and obd (on-board diagnostic) data

Abstract

The invention discloses an intelligent vehicle-mounted system realization method, and particularly relates to a user classification method based on adas (advanced driver assistant system) and obd (on-board diagnostic) data. An adas device, an obd module, and a Gps device are arranged on the vehicle for acquiring data. A communication module is arranged for realizing communication with the Internet. Through computing the information acquired by the above devices in a scientific computation method, the risk factor and the possibility of a driver can be judged, and a necessary prompt can be given. The method of the invention has the advantages that the risk probability of the driver can be accurately judged for mechanisms such as an insurance company, and the driver is correctly guided to form standard driving and other good habits.

Description

technical field [0001] The invention relates to an implementation method of an intelligent vehicle system, in particular to a user classification method based on adas and obd data. Background technique [0002] Advanced Driver Assistant System (ADAS), referred to as ADAS, is to use various sensors installed on the car to collect environmental data inside and outside the car at the first time, and to identify, detect and track static and dynamic objects And other technical processing, so that the driver can detect possible dangers in the fastest time, so as to attract attention and improve safety active safety technology. The sensors used in ADAS mainly include cameras, radars, lasers and ultrasonics, etc., which can detect light, heat, pressure or other variables used to monitor the state of the car. They are usually located in the front and rear bumpers, side mirrors, inside the steering column or windshield of the vehicle. on the glass. Early ADAS technology was mainly b...

AI Technical Summary

Problems solved by technology

[0004] Because the different habits of drivers will affect different risks of driving behavior, and insurance companies and other institutions cannot determine and grasp the risks of drivers, so insurance companies cannot accurately evaluate the insurance odds of drivers, often It causes an unfair and unreasonable premium rate, that is, drivers with good reinforcement habits cannot be reduced or exempted from insurance premiums, and drivers with a high accident rate cannot be given an increase in premium rates; therefore, for Institutions such as insurance companies need a scientific and reasonable evaluation method to accurately measure the driving habits of drivers to determine a reasonable insurance rate

At present, insurance companies have not yet had such a classification that can reasonably evaluate drivers, nor can they solve the problem of insurance rate differentiation

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