text: 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 .
  @cite_10: This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.
  @cite_18: This paper presents a feature-based machine vision system for estimating lane-departure of a traveling vehicle on a road. The system uses edge information to define an edge distribution function (EDF), the histogram of edge magnitudes with respect to edge orientation angle. The EDF enables the edge-related information and the lane-related information to be connected. Examining the EDF by the shape parameters of the local maxima and the symmetry axis results in identifying whether a change in the traveling direction of a vehicle has occurred. The EDF minimizes the effect of noise and the use of heuristics, and eliminates the task of localizing lane marks. The proposed system enhances the adaptability to cope with the random and dynamic environment of a road scene and leads to a reliable lane-departure warning system.
  @cite_13: Abstract Statistics show that worldwide motor vehicle collisions lead to significant deaths and disabilities as well as substantial financial costs to both society and the individuals involved. Unintended lane departure is a leading cause of road fatalities by the collision. To reduce the number of traffic accidents and to improve driver’s safety lane departure warning (LDW), the system has emerged as a promising tool. Vision-based lane detection and departure warning system has been investigated over two decades. During this period, many different problems related to lane detection and departure warning have been addressed. This paper provides an overview of current LDW system, describing in particular pre-processing, lane models, lane de Ntection techniques and departure warning system.
  @cite_2: 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.
summary: Image feature-based lane detection is a well researched area of computer vision @cite_13 . The majority of existing image-based methods use detected lane line features such as colors, gray-scale intensities, and textural information to perform edge detection. This approach is very sensitive to illumination and environmental conditions. On the Generic Obstacle and Lane Detection system proposed by Bertozzi and Broggi @cite_10 , lane detection was done using inverse perspective mapping to remove the perspective effect and horizontal black-white-black transaction. Their methodology was able to locate lane markings even in the presence of shadows or other artifacts in about 95 In 2005, Lee and Yi @cite_2 introduced the use of Sobel operator plus non-local maximum suppression (NLMS). It was built upon methods previously proposed by Lee @cite_18 proposing linear lane model and edge distribution function (EDF) as well as lane boundary pixel extractor (LBPE) plus Hough transform. The model was able to overcome weak points of the EDF based lane-departure identification (LDI) system by increasing lane parameters. The LBPE improved the robustness of lane detection by minimizing missed detections and false positives (FPs) by taking advantage of linear regression analysis. Despite improvements, the model performed poorly at detecting curved lanes.