In this paper, we present a novel model to detect lane regions and extract lane departure events (changes and incursions) from challenging, lower-resolution videos recorded with mobile cameras. Our algorithm used a Mask-RCNN based lane detection model as pre-processor. Recently, deep learning-based models provide state-of-the-art technology for object detection combined with segmentation. Among the several deep learning architectures, convolutional neural networks (CNNs) outperformed other machine learning models, especially for region proposal and object detection tasks. Recent development in object detection has been driven by the success of region proposal methods and region-based CNNs (R-CNNs). Our algorithm utilizes lane segmentation mask for detection and Fix-lag Kalman filter for tracking, rather than the usual approach of detecting lane lines from single video frames. The algorithm permits detection of driver lane departures into left or right lanes from continuous lane detections. Preliminary results show promise for robust detection of lane departure events. The overall sensitivity for lane departure events on our custom test dataset is 81.81 .
The objective of Lane Departure Prediction (LDP) is to predict if the driver is likely to leave the lane with the goal of warning drivers in advance of the lane departure so that they may correct the error before it occurs (avoiding a potential collision). This improves on LDW systems, which simply alert the driver to the error after it has occurred. LDP algorithms can be classified into one of the following three categories: vehicle-variable-based, vehicle-position estimation, and detection of the lane boundary using real-time captured road images. They all use real-time captured images @cite_17 .
@cite_17
2053131138
This paper presents a lane-departure identification (LDI) system of a traveling vehicle on a structured road with lane marks. As is the case with modified version of the previous EDF-based LDI approach [J.W. Lee, A machine vision system for lane-departure detection, CVIU 86 (2002) 52-78], the new system increases the number of lane-related parameters and introduces departure ratios to determine the instant of lane departure and a linear regression (LR) to minimize wrong decisions due to noise effects. To enhance the robustness of LDI, we conceive of a lane boundary pixel extractor (LBPE) capable of extracting pixels expected to be on lane boundaries. Then, the Hough transform utilizes the pixels from the LBPE to provide the lane-related parameters such as an orientation and a location parameter. The fundamental idea of the proposed LDI is based on an observation that the ratios of orientations and location parameters of left- and right-lane boundaries are equal to one as far as the optical axis of a camera mounted on a vehicle is coincident with the center of lane. The ratios enable the lane-related parameters and the symmetrical property of both lane boundaries to be connected. In addition, the LR of the lane-related parameters of a series of successive images plays the role of determining the trend of a vehicle's traveling direction and the error of the LR is used to avoid a wrong LDI. We show the efficiency of the proposed LDI system with some real images.