Innovative lane detection method to increase the accuracy of lane departure warning system

Lane departure warning is one important feature in Advanced Driver Assistance Systems (ADAS), which aims to improve overall safety on the road. However, challenges such as inconsistent shadows and fading lane markings often plague the road surface and cause the lane detection system to produce false warnings. Users are aggravated by the warning and tend to disable this safety feature. This paper proposes an efficient Gabor filtering-based lane detection method to overcome the aforementioned conditions and improves the accuracy of lane departure warning system. Furthermore, it serves as a cost-effective solution to a lane departure warning problem, allowing it to be widely deployed. It is heuristically found that lane marking has a general directional property, which can be further enhanced by Gabor filter while suppressing inconsistent road shadows and road markers. Enhanced lane markings are then subjected to adaptive canny edge detection to extract distinct edge markings. Lastly, Hough transformation is applied to label the correct lane candidates on the road surface. Furthermore, we generate a dataset of Malaysia road with various driving conditions. As a proof of concept, a lane departure warning system is built based on the proposed lane detection method, which is able to achieve an accuracy of 93.67% for lane detection and 95.24% for lane departure warning tested on our challenging dataset. The codes are implemented on Raspberry pi 3B and installed in a vehicle for real-time application. The codes are multithreaded and found to achieve a desirable frame speed of 20 fps at 75% CPU utilization.