Vehicle yaw detection method, computer equipment, storage medium and vehicle system

Abstract

The invention provides a vehicle yaw detection method, computer equipment, a storage medium and a vehicle system. The vehicle yaw detection method comprises the steps of: sampling a planned path and an actual path of a vehicle, and obtaining a planned path point set and an actual path point set of vehicle travelling; taking any point S from the actual path point set, searching all planned pathpoints adjacent to the point S from the planned path point set, calculating the superposition rate P among the point S and all the adjacent planned path points, then, storing the superposition rate P in a superposition rate set, and removing the point S from the actual path point set; circulating the second step till the actual path point set is an empty set; and, obtaining the average value P of all the superposition rates in the superposition rate set, and judging that a vehicle yaws if the P is less than a pre-set threshold value. The superposition rate is introduced, so thatvehicle line yaw detection is realized; in a travelling process, only data needs to be stored; real-time calculation is not required; and thus, the calculation pressure of a server is reduced. The vehicle yaw detection method, the computer equipment, the storage medium and the vehicle system in the invention are applied to the field of vehicle yaw detection.

Description

technical field [0001] The invention relates to the field of vehicle yaw detection, in particular to a vehicle yaw detection method, computer equipment, storage media and a vehicle system. Background technique [0002] Line yaw detection is generally used in systems that have fixed requirements for vehicle trajectories. For example, in the Industrial Internet of Things, prevent pump trucks from exceeding the specified route during driving and stealing oil when driving on other routes. There are roughly three current route yaw detection methods: (1) real-time calculation based on latitude and longitude to determine whether the track is on the preset route; (2) judging whether the driving track is on the preset street according to the historical track and street name; (3) based on the vehicle Travel time and preset route length to judge whether to drive off-track. The above three line yaw detection methods all have obvious shortcomings, such as poor fault tolerance, occasiona...

AI Technical Summary

Problems solved by technology

The above three line yaw detection methods all have obvious shortcomings, such as poor fault tolerance, occasional large deviations in latitude and longitude information, and false alarms will occur when the latitude and longitude information is inaccurate

Using street names or driving time to judge is easily affected by map street information and vehicle conditions, and may also generate false alarms

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