Robust Lane Detection Method Research Articles

Robust Lane Detection Method Based on Dynamic Region of Interest

Lane detection is an important component of vehicle assisted driving systems. Many lane line algorithms have been proposed, but lane line detection is still a challenging task in environments with road text or vehicle interference. To address these issues, this paper proposes a robust lane line detection method under structured roads. First, we propose a method based on the slope and length of a straight line and the distance of the line from the centre line to extract regions of interest reducing interference from other invalid regions. Afterwards, we use a new fusion method to fuse the extracted colour features with the gradient features, enhancing the lane features while greatly reducing the interference in the pixels. Next, we cut the extracted pixel feature maps in equal parts into several sub-maps and perform pixel statistics for each sub-map, replacing sub-maps with abnormal pixel values with adjacent sub-maps, which can effectively reduce noise. Finally, we use DBSCAN to cluster the pixels and fit the lane lines with least squares. The experimental results show that the method can effectively detect lane lines and has good robustness.

RoaDSaVe: A Robust Lane Detection Method Based on Validity Borrowing From Reliable Lines

Lane detection is a critical but challenging task in autonomous driving, especially in complex scenes. In most of the complicated scenes, only a part of the scene is challenged, and the whole road surface is not involved. We believe that perception of the scene’s structure and using information of reliable areas to detect lanes in challenging areas is the key to robust lane detection. This paper proposes a novel lane detection method that can provide a reliable system even in complex situations and meet real-time requirements, called RoaDSaVe. The RoaDSaVe method consists of three major modules: scene awareness, physical inference, and validity effectiveness. The scene awareness module takes advantage of an effective combination of shallow and deep features in a neural network, resulting in a more accurate acquisition of spatial and semantic feature maps. Following that, an optimization method is used to obtain the physical parameters of the lane lines based on road and lane semantic labels. The physical inference module creates an adjusted histogram and considers locations with high scores and reliability to be valid lines. In the validity effectiveness module, the potential points are stimulated by the valid lines. If a potential line did not achieve sufficient validity in the previous module, it can still be considered valid if it meets the required score. The experiments on several public datasets show that the RoaDSaVe lane detection method has significant ability in scenes with local challenges and achieves excellent performance compared to the state-of-the-art methods.

Robust Lane Detection Algorithm for Autonomous Trucks in Container Terminals

Container terminal automation offers many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A novel lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure fast and accurate lane positioning. A robust lane detection method can deal with complicated detection problems in realistic road scenarios. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions; in addition, the robustness of the lane detection method with stochastic noise is tested.

RS-Lane: A Robust Lane Detection Method Based on ResNeSt and Self-Attention Distillation for Challenging Traffic Situations

Lane detection plays an essential part in advanced driver-assistance systems and autonomous driving systems. However, lane detection is affected by many factors such as some challenging traffic situations. Multilane detection is also very important. To solve these problems, we proposed a lane detection method based on instance segmentation, named RS-Lane. This method is based on LaneNet and uses Split Attention proposed by ResNeSt to improve the feature representation on slender and sparse annotations like lane markings. We also use Self-Attention Distillation to enhance the feature representation capabilities of the network without adding inference time. RS-Lane can detect lanes without number limits. The tests on TuSimple and CULane datasets show that RS-Lane has achieved comparable results with SOTA and has improved in challenging traffic situations such as no line, dazzle light, and shadow. This research provides a reference for the application of lane detection in autonomous driving and advanced driver-assistance systems.

A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation.

Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.

Robust Lane-Detection Method for Low-Speed Environments.

Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.

A robust lane detection method based on hyperbolic model

Lane detection is an essential part of safety assurance in intelligent vehicle and advanced driver assistance systems. Despite many methods having been proposed, there still remain challenges such as complex road surface and large curvature. In this paper, we present a robust lane detection method under structured roads to solve these issues. The method contains two parts: straight line detection in near field and curve matching in far field. Instead of generating top-view image by inverse perspective mapping (IPM), we propose a new form of IPM application to reduce noise that we only take advantage of sub-pixel-level spatial relations and project line segments obtained by line segments detector to top-view image. Then, we apply density-based spatial clustering of applications with noise to clustering segments and design a fusion method to extract the optimal lines combination from clusters. Finally, a weighted hyperbolic model is proposed to finish curve fitting. The results of experiment indicate that the method has robust performance in complex environment.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial–temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

A Robust Lane Detection Method Based on Vanishing Point Estimation Using the Relevance of Line Segments

In this paper, a robust lane detection method based on vanishing point estimation is proposed. Estimating a vanishing point can be helpful in detecting lanes, because parallel lines converge on the vanishing point in a projected 2-D image. However, it is not easy to estimate the vanishing point correctly in an image with a complex background. Thus, a robust vanishing point estimation method is proposed that uses a probabilistic voting procedure based on intersection points of line segments extracted from an input image. The proposed voting function is defined with line segment strength that represents relevance of the extracted line segments. Next, candidate line segments for lanes are selected by considering geometric constraints. Finally, the host lane is detected by using the proposed score function, which is designed to remove outliers in the candidate line segments. Also, the detected host lane is refined by using inter-frame similarity that considers location consistency of the detected host lane and the estimated vanishing point in consecutive frames. Furthermore, in order to reduce computational costs in the vanishing point estimation process, a method using a lookup table is proposed. Experimental results show that the proposed method efficiently estimates the vanishing point and detects lanes in various environments.

Model-Based Robust Lane Detection for Driver Assistance

In this paper, we propose an efficient and robust lane detection method for detecting immediate left and right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lane model and the reliable line segment clustering. The reliable line segment cluster is determined from the most probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environments such as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, and that the proposed method performs better compared with other lane detection methods on an CMU/VASC lane dataset.

경사진 도로 환경에서도 강인한 실시간 차선 검출방법

본 논문에서는 영상의 카메라 파라미터가 필요 없는 역 투시변환 기술 및 제안한 차선필터를 사용하여 경사진 도로 환경에서도 강인한 실시간 차선 검출방법을 제안한다. 영상의 시작 프레임에서 소실점을 찾은 후, 소실점 주변의 일정영역을 템플릿(TA: Template Area)으로 저장하며, 소실점을 기준으로 하단으로 내려가면서 차선을 예측하고, 예측된 차선을 기반으로 역 투시변환계수를 추출하여 추출된 계수로 원근감이 제거된 영상을 얻으며, 바로 그 영상에 제안한 차선필터를 적용하여 차선을 검출한다. 경사진 도로환경에서도 강인한 차선 검출을 위하여 입력영상으로 부터 TA와 유사한 영역(SA: Similar Area)을 템플릿 매칭으로 추적하여 소실점을 재계산하여 차선을 검출한다. 제안한 방법은 경사진 도로 환경에서도 차선검출이 견고하며, 처리영역을 축소하고 처리과정을 단순화함으로서 초당 40 frames 정도의 양호한 차선검출 결과를 보였다. In this paper, we propose a novel method for real-time lane detection that is robust for inclined roads and not require a camera parameter, the Inverse Perspective Transform of the image, and the proposed lane filter. After finding the vanishing point from the start frame of the image and storing the region surrounding the vanishing point as the Template Area(TA), our method predict the lanes by scanning toward the lower part from the vanishing point of the image and obtain the image removed the perspective effect using the Inverse Perspective Transform coefficients extracted based on the predicted lanes. To robustly determine lanes on inclined roads, the region surrounding the vanishing point is set up as the template area (TA), and, by recalculating the vanishing point by tracing the area similar to the TA (SA) in the input image through template matching, it responds to the changes on the road conditions. The proposed method for a more robust lane detection method for inclined roads is a lane detection method by applying a lane detection filter on an image removed of the perspective effect. Through this method, the processing region is reduced and the processing procedure is simplified to produce a satisfactory lane detection result of about 40 frames per second.

The Research of the Lane Detection Algorithm Base on Vision Sensor

The intelligent vehicle is an important area country in recent years of painstaking research in Intelligent Transportation System, which become the focus of the study, based on the visual structure of the road environment recognition. Aiming at the robust and real time problems of lane detection in the visual navigation system of intelligent vehicles, a robust lane detection method is proposed for the structured road. It can provide for intelligent vehicle automatically to maintain lane and changing lanes traveling lane information necessary to make smart vehicle to achieve a smooth, safe driving. Due to the complexity of the road itself, the complexity of the road image, Therefore, the pre-road established certain assumptions and these assumptions and the detection algorithm is combined to further improve the algorithm efficiency. Simulation test of the collected road images results show that the lane detection method designed in this study is stable enough to show the lane Position for engineering application not matter in good or poor illumination road condition.

PSO Algorithm Particle Filters for Improving the Performance of Lane Detection and Tracking Systems in Difficult Roads

In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options.

A lane detection algorithm for personal vehicles

AbstractBy the term “personal vehicle,” we mean a simple and lightweight vehicle expected to emerge as a personal ground transportation device. The motorcycle, electric wheelchair, and motor‐powered bicycle are examples of the personal vehicle and have been developed for personal transportation use. Recently, a new type of intelligent personal vehicle called the Segway has been developed, which is controlled and stabilized by using on‐board intelligent multiple sensors. The demand for such personal vehicles is increasing: (1) to enhance human mobility, (2) to support mobility for elderly persons, and (3) to reduce environmental load. With the rapid growth of the personal vehicle market, the number of accidents caused by human error is also increasing. These accidents are associated with driving capabilities; to enhance or support driving capabilities as well as to prevent accidents, intelligent assistance is necessary. One of the most important elementary functions for personal vehicles is robust lane detection. In this paper, we develop a robust lane detection method for personal vehicles in outdoor environments. The proposed lane detection method employs a 360° omnidirectional camera and unique robust image processing algorithm. In order to detect lanes, a combination of the template matching technique and the Hough transform is employed. The validity of the proposed lane detection algorithm was confirmed with a prototype vehicle under various types of sunshine conditions. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 177(4): 23–32, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21193

A Study of Lane Detection Algorithm for Personal Vehicle

By the word “Personal vehicle”, we mean a simple and lightweight vehicle expected to emerge as personal ground transportation devices. The motorcycle, electric wheelchair, motor-powered bicycle, etc. are examples of the personal vehicle and have been developed as the useful for transportation for a personal use. Recently, a new types of intelligent personal vehicle called the Segway has been developed which is controlled and stabilized by using on-board intelligent multiple sensors.The demand for needs for such personal vehicles are increasing, 1) to enhance human mobility, 2) to support mobility for elderly person, 3) reduction of environmental burdens. Since rapidly growing personal vehicles' market, a number of accidents caused by human error is also increasing. The accidents are caused by it's drive ability. To enhance or support drive ability as well as to prevent accidents, intelligent assistance is necessary. One of most important elemental functions for personal vehicle is robust lane detection.In this paper, we develop a robust lane detection method for personal vehicle at outdoor environments. The proposed lane detection method employing a 360 degree omni directional camera and unique robust image processing algorithm. In order to detect lanes, combination of template matching technique and Hough transform are employed. The validity of proposed lane detection algorithm is confirmed by actual developed vehicle at various type of sunshined outdoor conditions.

A robust lane detection and tracking method based on computer vision

This paper presents a robust method designed to detect and track a road lane from images provided by an on-board monocular monochromatic camera. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori formulation of the lane detection problem, and a Tabu search algorithm to maximize the posterior density. The model parameters completely determine the position of the host vehicle within the lane, its heading direction and the local structure of the lane ahead. Based on the lane detection result in the first frame of the image sequence, a particle filter, having multiple hypotheses capability and performing nonlinear filtering, is used to recursively estimate the lane shape and the vehicle position in the sequence of consecutive images. Experimental results reveal that the proposed lane detection and tracking method is robust against broken lane markings, curved lanes, shadows, strong distracting edges, and occlusions in the captured road images.