Method and system for identifying lateral driving conditions of a vehicle

Abstract

The invention provides a method for identifying the lateral driving condition of a vehicle, comprising: identifying the steering process of the vehicle; calculating the identification data of the operating condition in the identified steering process, including the change angle of the heading angle, the course angle history curve and The weighted distance sum, the maximum yaw rate and the lateral displacement between the preset typical lane change template curves; the weighted distance sum is obtained based on the dynamic warping algorithm; based on the calculated working condition identification data, the driving condition to which the steering process belongs is determined , including turning driving conditions, U-turn driving conditions and lane changing driving conditions. The invention also provides a system for identifying the lateral running condition of the vehicle. Because the present invention integrates the dynamic time warping algorithm to identify the lane-changing driving condition in the lateral driving condition of the vehicle, the recognition method has good adaptability and high recognition accuracy, and the precision rate of the lane-changing working condition reaches more than 90%. , so that the driver's manipulation of the vehicle can be more accurately determined.

Description

technical field [0001] The invention relates to a method and a system for identifying a vehicle running condition, in particular to a method and a system for identifying a vehicle's lateral running condition. Background technique [0002] Vehicle driving condition recognition is an important basic work for driving style recognition and driving behavior analysis. By identifying the driving conditions of the vehicle, it is possible to determine the process of the driver's manipulation of the vehicle, and then analyze the driving behavior and driving style. [0003] In the past recognition of vehicle driving conditions, there are traffic environment signals obtained through vehicle-mounted smart cameras, based on lane markings and vehicle position changes relative to lane lines to identify vehicle driving conditions, and there is also a method of using vehicle chassis CAN communication A method for identifying vehicle status signals in a network. However, due to the low assem...

AI Technical Summary

Problems solved by technology

However, due to the low assembly rate of smart cameras in vehicles, they cannot be widely used. In addition, the traditional method of using chassis CAN signals to identify lateral driving conditions has low accuracy and is difficult to meet the needs of driving style and driving behavior.

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