/Systems And Methods For Detecting Lane Crossings And Classifying Lane Changes
Abstract

A device may receive forward facing video data associated with a vehicle, and may process the forward facing video data, with neural network models, to detect lane lines and to determine classifications for the lane lines. The device may utilize the forward facing video data to generate a histogram of horizontal positions of the vehicle, and may fit probability density functions on the histogram to calculate a mean and a standard deviation. The device may utilize the mean and the standard deviation to identify a crossing interval, and may classify the forward facing video data as a lane crossing or a lane change based on the crossing interval. The device may calculate a lane crossing score or may calculate a lane change score. The device may perform actions based on the lane crossing score or the lane change score.

Full Text

What is claimed is:

A device may receive forward facing video data associated with a vehicle, and may process the forward facing video data, with neural network models, to detect lane lines and to determine classifications for the lane lines. The device may utilize the forward facing video data to generate a histogram of horizontal positions of the vehicle, and may fit probability density functions on the histogram to calculate a mean and a standard deviation. The device may utilize the mean and the standard deviation to identify a crossing interval, and may classify the forward facing video data as a lane crossing or a lane change based on the crossing interval. The device may calculate a lane crossing score or may calculate a lane change score. The device may perform actions based on the lane crossing score or the lane change score.
Timeline
Filed
05/20/2026
Published
09/17/2026
Granted
Not Available
IPC Codes(5)
G06V 20/56:exterior to a vehicle by using sensors mounted on the vehicle
B60R 1/22:for viewing an area outside the vehicle, e.g. the exterior of the vehicle
G06V 10/50:by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis