Driver manipulation behavior quantification method and device based on least action principle

Abstract

The invention discloses a driver manipulation behavior quantification method and device based on the least action principle. The driver manipulation behavior quantification method comprises the stepsthat S1, a driver manipulation mechanism quantitative description module is pre-arranged in an electronic control unit of a vehicle and comprises the action S<Risk> simultaneously reflecting the traffic risk and the passing efficiency of driving manipulation behavior of a driver; S2, vehicle and traffic environment information synchronous with the time is obtained through an information collectingdevice on the vehicle; and S3, according to the vehicle and traffic environment information synchronous with the time, the current traveling guidance speed is obtained through S<Risk> in the driver manipulation mechanism quantitative description module, and the value of the action S<Risk> is minimized. According to the driver manipulation behavior quantification method and device based on the least action principle, the least action principle is utilized to describe the balance about the risk and the efficiency in the process that the driver drives the vehicle, and the driving characteristicsof pursuing interests and avoiding risks in the driving process of any driver can be described quantitatively.

Description

technical field [0001] The invention relates to the technical field of smart car applications, in particular to a method and device for quantifying driver manipulation behavior based on the principle of least action. Background technique [0002] Road traffic safety is related to the human-vehicle-environment closed-loop system. Among the three elements of human, vehicle, and environment that constitute traffic accidents, the human factor usually accounts for the largest proportion, that is, the vast majority of traffic accidents are caused by human factors. Therefore, the driver's manipulation of the vehicle has become a key factor in ensuring traffic safety. At present, for the rapid development of intelligent transportation, intelligent driving technology and intelligent vehicles, the safety performance and driver acceptance of the intelligent vehicle system are important factors restricting the intelligentization of the vehicle. The driver's acceptance refers to the inte...

AI Technical Summary

Problems solved by technology

However, because the driver's driving behavior itself has various differences such as individual differences, age group differences, gender differences, and regional differences, the use of existing research methods often requires a large number of data samples, which brings great challenges to the research. Difficulties

[0004] In addition to the above-mentioned differences in the driving behavior of different drivers, the current automotive intelligent system is also limited by the complexity of the road environment, the differences in driving behavior, and the variability of driving conditions. High false alarm rate, poor acceptability and other issues

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