Method and system for identifying lateral driving condition of vehicle

Abstract

The invention provides a method for identifying a lateral driving condition of a vehicle. The method includes the following steps: identifying a vehicle steering process; calculating condition identification data in the identified steering process, wherein the condition identification data includes the sum of weighted distance among a heading angle change angle, a heading angel history curve and apreset typical lane change template curve, maximum yaw angular velocity and lateral displacement, and the sum of weighted distance is obtained based on a dynamic regularization algorithm; and determining a driving condition to which the steering process belongs based on the calculated condition identification data, wherein the driving condition includes a turning driving condition, a U-turn driving condition and a lane changing driving condition. The invention also provides a system for identifying the lateral driving condition of the vehicle. Since the lane changing driving condition in thevehicle lateral driving condition is identified by combining the dynamic time regularization algorithm, the identification method has good adaptability and high identification accuracy, and the accuracy rate of lane changing conditions is more than 90%, thereby more accurately determining the driver's control condition of the vehicle.

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.

Images