Real-time lane departure warning with cascade lane segmentation

Abstract

Lane departure warning (LDW) is one of the safety innovations in autonomous cars that provides vehicle position monitoring. This technology will alarm if the vehicle moves out of the lane. Lane detection and lane measurement is the main part of LDW. The novelty of this research is the method can measure different lane marks. Very important to know the lane mark that can and cannot be passed. We use semantic segmentation to segment lane mark solid, lane mark dashed, and road. After extracting road and lane marks, we use inverse perspective mapping (IPM) to help calculate the measurement between the car and lane mark. The data is that 374 images were collected from several roads in Makassar City. The model was evaluated using intersection over union (IoU), reaching 79.8% accuracy. The developed system also estimates the measure between the vehicle and lane marks.
The lane measurement estimation system’s test results were evaluated using the root mean square error (RMSE) method to reach between 0.025254 and 0.134345.