CAN data-based driver comprehensive quality evaluation method

Abstract

The present invention relates to a CAN data-based driver comprehensive quality evaluation method. The method comprises a first step of acquiring a latitude and a longitude, an engine rotation speed, a vehicle speed, a steer wheel rotation angle, an oil consumption, a throttle angle, and a brake pedal state, and calculating instantaneous oil consumption, acceleration, and a ratio of the engine rotation speed to the speed; a second step of determining evaluation factors, including a steering wheel rotation angle entropy H (theta), a steering wheel rotation angle rate V<theta>, a motor vehicle speed entropy H (v), an acceleration absolute value a, an acceleration strength V, a positive acceleration a, and a ratio phi of the engine rotation speed to themotor vehicle speed; a third step of establishing weight alpha of a single factor in evaluation of the driver comprehensive quality by using a vague layer analysis method, and establishing a desired weight vector Q; a fourth step of establishing as few as membership functions, and resolving a single factor vague determining matrix R; and a fifth step of resolving a determining vector theta=Q*R, and analyzing and resolving to obtain a determining set for evaluating the driver comprehensive quality. The method combines quantitative algorithm and qualitative algorithm, so as to evaluate the driver comprehensive quality and improve the evaluation accuracy.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a driver's comprehensive quality evaluation method based on CAN data. Background technique [0002] With the development of the economy and the progress of society, cars are gradually popularized in the homes of ordinary people. But at the same time, traffic accidents are also on the rise. The main reason is that the contradictions between people, cars and roads are becoming increasingly intensified. According to statistics from European and American countries, about 85% of traffic accidents are related to drivers among the causes of traffic accidents. In domestic traffic accident investigation statistics, it also shows that 80%-90% of accidents are caused by human factors. Therefore, it is of great significance to study the behavior of motor vehicle drivers, evaluate them, persuasion and education to drivers, and ensure traffic safety. [0003] Traditional traffic de...

AI Technical Summary

Problems solved by technology

In order to make full use of the collected data, more evaluation indicators need to be excavated to provide a more sufficient basis for analyzing driver behavior. However, based on these data indicators, we cannot quantitatively evaluate the driver's overall quality

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