Method and device for quantifying driver's manipulation behavior based on principle of least action

Abstract

The present invention discloses a method and device for quantifying the driver's manipulation behavior based on the principle of least action. The quantification method for the driver's manipulation behavior includes: S1, setting a driver's manipulation mechanism quantitative description module in advance in the electronic control unit of the ego vehicle, so that The quantitative description module of the driver’s manipulation mechanism includes SRisk, which simultaneously reflects the traffic risk and traffic efficiency of the driver’s driving manipulation behavior; S2, through the information collection device on the self-vehicle, obtains the self-vehicle and traffic environment information synchronized with time ; S3, according to the self-vehicle and traffic environment information synchronized with time, through the driver's manipulation mechanism to quantitatively describe SRisk in the module, obtain the current driving guidance speed, so that the value of the action amount SRisk is minimized, and the present invention utilizes the minimum action The quantitative principle describes the trade-off between risk and efficiency in the process of driving a vehicle, and can quantitatively describe the driving characteristics of any driver who seeks advantages and avoids disadvantages in the process of driving.

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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