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 .
More recently, in @cite_5 , they used a Dual-View Convolutional Neural Network (DVCNN) with hat-like filter and optimized simultaneously the frontal-view and the top-view cameras. The hat-like filter extracts all potential lane line candidates, thus removing most of FPs. With the front-view camera, FPs such as moving vehicles, barriers, and curbs were excluded. Within the top-view image, structures other than lane lines such as ground arrows and words were also removed.
@cite_5
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In this paper, we propose a Dual-View Convolutional Neutral Network (DVCNN) framework for lane detection. First, to improve the low precision ratios of literature works, a novel DVCNN strategy is designed where the front-view image and the top-view one are optimized simultaneously. In the front-view image, we exclude false detections including moving vehicles, barriers and curbs, while in the top-view image non-club-shaped structures are removed such as ground arrows and words. Second, we present a weighted hat-like filter which not only recalls potential lane line candidates, but also alleviates the disturbance of the gradual textures and reduces most false detections. Third, different from other methods, a global optimization function is designed where the lane line probabilities, lengths, widths, orientations and the amount are all taken into account. After the optimization, the optimal combination composed of true lane lines can be explored. Experiments demonstrate that our algorithm is more accurate and robust than the state-of-the-art. Lane keeping is an important feature for self-driving cars. This paper presents an end-to-end learning approach to obtain the proper steering angle to maintain the car in the lane. The convolutional neural network (CNN) model takes raw image frames as input and outputs the steering angles accordingly. The model is trained and evaluated using the comma.ai dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection, path planning and steering control, the end-to-end model can directly steer the vehicle from the front view camera data after training. It learns how to keep in lane from human driving data. Further discussion of this end-to-end approach and its limitation are also provided. Navigation and obstacle avoidance are two problems that are not easily incorporated into direct control of autonomous vehicles solely based on visual input. However, they are required if lane following given proper lane markings is not enough to incorporate trained systems into larger architectures. We present a method to allow for obstacle avoidance while driving using a single, front-facing camera as well as navigation capabilities such as taking turns at junctions and lane changes by feeding turn indicator signals into a Convolutional Neural Network. Both situations share the difficulty intrinsic to single camera setups of limited field of views. This problem is handled by using a spatial history of input images to extend the field of view regarding static obstacles. The trained model, referred to as DriveNet, is evaluated in real world driving scenarios, using the same model for lateral vehicle control to both dynamically drive around obstacles as well as perform lane changing and turning in intersections. We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.