Robust Lane Detection Research Articles

Robust lane detection algorithm based on triangular lane model

Recently, various technologies and sciences are developed for fourth industrial revolution which includes artificial intelligence, robotics, internet of things, 3-D printing, and autonomous transportation. The autonomous transportation is one of the fourth industrial technology, and has been studying for safety driving. Accurate lane detection and lane departure warning system is very important for autonomous transportation. However, conventional methods have some problems of applying real-driving such as extracting miss lanes under cloudy weather and vehicle disturbance situations. In order to solve the real driving problems, we propose a new lane detection technique for lane departure and forward collision warning system using a single in-vehicle camera. The proposed method consists of triangular lane model, feature points extraction method. In the near field, a triangular lane model is used to approximate a pair of lane boundaries. Subsequently, feature points extraction method based on hyperbola curve model is applied to obtain lane curvature in the far field. Mathematical B-spline applied to feature points for curved lane fitting. Simulation results show that the proposed lane detection and tracking method has good performance.

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.

Image processing-based framework for continuous lane recognition in mountainous roads for driver assistance system

This paper presents a robust lane detection technique for roads on hilly terrain. The target of this paper is to utilize image processing strategies to recognize lane lines on structured mountain roads with the help of improved Hough transform. Vision-based approach is used as it performs well in a wide assortment of circumstances by abstracting valuable information contrasted with other sensors. The proposed strategy processes the live video stream, which is a progression of pictures, and concentrates on the position of lane markings in the wake of sending the edges through different channels and legitimate thresholding. The algorithm is tuned for Indian mountainous curved and paved roads. A technique of computation is utilized to discard the disturbing lines other than the credible lane lines and show just the required prevailing lane lines. This technique will consequently discover two lane lines that are nearest to the vehicle in a picture as right on time as could reasonably be expected. Various video sequences on hilly terrain are tested to verify the effectiveness of our method, and it has shown good performance with a detection accuracy of 91.89%.

Lane detection by estimating and using restricted search space in Hough domain

Lane detection is one of the key elements for driving assistance systems. In this paper, a robust lane detection algorithm using restricted search space in Hough domain is presented. Although, it is not necessary to restrict the search space, many algorithms apply some restriction on some parameters (i.e. expected Vanishing point (Vp) position and the maximum deviation on Vp in both horizontal and vertical direction, expected road width (in pixels) and the maximum deviation on its width) to eliminate some of the unlikely detection results and prune some of the misdetections for more accurate results. However, conventionally, these parameters are manually selected and given to the algorithm as an input and same parameters cannot be used for different experimental set-up. In this algorithm, an alternative approach is proposed to automate this process. Novelty on this paper is in two folds. First, the proposed algorithm uses a set of road images for a given experimental set-up and statistically estimates both expected position and the expected deviation on Vp, and the road width. Second, a mask is created in Hough domain to efficiently apply necessary restrictions to both Road width and the Vp position. While this approach is applicable to any video sequence taken by any experimental set-up, it also benefits from restricting search space for more accurate detection results.The proposed algorithm is tested for 1000 highway road images and for only 28 road markings are miss detected. Revealing detection rate of more than 97%.

가우시안 혼합모델을 이용한 강인한 실시간 곡선차선 검출 알고리즘

ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.

Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting

With the increase in the number of vehicles, many intelligent systems have been developed to help drivers to drive safely. Lane detection is a crucial element of any driver assistance system. At present, researchers working on lane detection are confronted with several major challenges, such as attaining robustness to inconsistencies in lighting and background clutter. To address these issues in this work, we propose a method named Lane Detection with Two-stage Feature Extraction (LDTFE) to detect lanes, whereby each lane has two boundaries. To enhance robustness, we take lane boundary as collection of small line segments. In our approach, we apply a modified HT (Hough Transform) to extract small line segments of the lane contour, which are then divided into clusters by using the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm. Then, we can identify the lanes by curve fitting. The experimental results demonstrate that our modified HT works better for LDTFE than LSD (Line Segment Detector). Through extensive experiments, we demonstrate the outstanding performance of our method on the challenging dataset of road images compared with state-of-the-art lane-detection methods.

장애물 격자지도 기반 가상차선 추정 기법

Navigation in outdoor environments is a fundamental and challenging problem for unmanned ground vehicles. Detecting lane markings or boundaries on the road may be one of the solutions to make navigation easy. However, because of various environments and road conditions, a robust lane detection is difficult. In this paper, we propose a new approach for estimating virtual lanes on a traversable region. Estimating the virtual lanes consist of two steps: (i) we detect virtual road region through road model selection based on traversability at current frame and similarity between the interframe and (ii) we estimate virtual lane using the number of lane on the road and results of previous frame. To improve the detection performance and reduce the searching region of interests, we use a probability map representing the traversability of the outdoor terrain. In addition, by considering both current and previous frame simultaneously, the proposed method estimate more stable virtual lanes. We evaluate the performance of the proposed approach using real data in outdoor environments.

Improved river flow and random sample consensus for curve lane detection

Accurate and robust lane detection, especially the curve lane detection, is the premise of lane departure warning system and forward collision warning system. In this article, an algorithm based on improved river flow and random sample consensus is proposed to detect curve lane under challenging conditions including the dashed lane markings and vehicle occlusion. The curve lanes are modeled as hyperbola pair. To determine the coefficient of curvature, an improved river flow method is presented to search feature points in the far vision field guided by the results of detected straight lines in near vision field or the curved lines from the last frame, which can connect dashed lane markings or obscured lane markings. As a result, it is robust on dashed lane markings and vehicle occlusion conditions. Then, random sample consensus is utilized to calculate the curvature, which can eliminate noisy feature points obtained from improved river flow. The experimental results show that the proposed method can accurately detect lane under challenging conditions.

Probabilistic lane detection and lane tracking for autonomous vehicles using a cascade particle filter

This paper proposes a robust lane detection algorithm with a cascade particle filter that incorporates a model decomposition approach. Despite the sophisticated tracking mechanism of a particle filter, the conventional particle-filter-based lane detection system suffers from an estimation accuracy problem and a high computational load. In order to improve the robustness and the computation time for lane detection systems, the proposed cascade particle filter decomposes a lane model into two submodels: a straight model and a curve model. By dividing the lane model, not only can the computation time be decreased, but also the accuracy of the lane state estimation system can be increased. The proposed lane detection algorithm and the cascade particle filter were evaluated on various roads and environmental conditions with the autonomous vehicle A1, which was the winner of the 2010 and 2012 Autonomous Vehicle Competition in the Republic of Korea organized by the Hyundai motor group. The proposed algorithm proved to be sufficiently robust and fast to be applied to autonomous vehicles as well as to intelligent vehicles for improving the vehicle safety.

A Real Time Method to Detect Vehicle for Collision Avoidance Applications

In this paper, we propose a real time method to detect vehicles on road by using a vehicle mounted monocular camera. Based on lane detection design, low time cost and high accuracy vehicle detecting and tracking algorithm is achieved. Robust and fast lane detection has been achieved using Hough transform in combination with a line merging method. The lane data are used for effective vehicle hypothesis generation. A subsequent validation, based on the area ratio of hypothesis vehicle, is used to eliminate false positives. Further, a data fusion mechanism is proposed to incorporate temporal information for stably updating vehicle detection results over time. Experimental results show that, without specific hardware and software optimizations, our method is able to detect vehicles on road with low false alarm rate at real time speeds of 30 frames per second.

Lane Detection and Tracking Using Classification in Image Sequences

We propose a novel lane detection method based on classification in image sequences. Both structural and statistical features of the extracted bright shape are applied to the neural network for finding correct lane marks. The features used in this paper are shown to have strong discriminating power to locate correct traffic lanes. The traffic lanes detected in the current frame is also used to estimate the traffic lane if the lane detection fails in the next frame. The proposed method is fast enough to apply for real-time systems; the average processing time is less than 2msec. Also the scheme of the local illumination compensation allows robust lane detection at nighttime. Therefore, this method can be widely used in intelligence transportation systems such as driver assistance, lane change assistance, lane departure warning and autonomous vehicles.

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.

굴곡진 도로에서의 구간 선형 모델을 이용한 차선 검출

본 논문에서는 굴곡진 도로를 구간 선형 모델로 근사화한 차선 검출 알고리즘을 제안한다. 기존의 차선 검출 알고리즘들은 지표면이 평면이라는 가정을 이용하기 때문에, 도로면이 굴곡진 실제 도로에서는 강건한 차선 검출이 어렵다. 제안하는 방법에서는 이 문제를 전체 차로를 구간으로 분할하고, 각 구간 내에서 차로를 가장 잘 근사하는 평면 차로를 구함으로써 해결한다. 이를 위해 각 구간 내에서 다양한 각도와 위치를 가지는 평면 형태의 구간 차로 후보들을 생성하였다. 구간 차로 후보들의 연결 조합 중 실제 차로에 가장 가까운 조합을 다이나믹프로그래밍을 이용하여 찾음으로써 굴곡진 차로를 근사한다. 평면 도로 뿐 아니라, 상하, 좌우의 굴곡이 있는 도로 영상으로 구성된 데이터세트에 대하여 제안하는 방법의 차선 검출 성능을 검증하였다. 평면 도로를 가정한 기존의 방법들이 80%에서 90% 초반의 검출률을 보이는 반면, 제안하는 방법은 90% 후반의 검출률을 보임을 통해 굴곡진 도로에서의 차선 검출의 강건성을 입증하였다. This paper proposes a robust lane detection algorithm for non-flat roads by combining a piecewise linear model and dynamic programming. Compared with other lane models, the piecewise linear model can represent 3D shapes of roads from the scenes acquired by monocular camera since it can form a curved surface through a set of planar road. To represent the real road, the planar roads are created by various angles and positions at each section. And dynamic programming determines an optimal combination of planar roads based on lane properties. Experiment results demonstrate the robustness of proposed algorithm against non-flat road, curved road, and camera vibration.

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.

특징점 병합과 카메라 외부 파라미터 추정 결과를 고려한 B-snake기반 차선 검출

This paper proposes a robust lane detection algorithm for bumpy or slope changing roads by estimating extrinsic camera parameters, which represent the pose of the camera mounted on the car. The proposed algorithm assumes that two lanes are parallel with the predefined width. The lane detection and the extrinsic camera parameter estimation are performed simultaneously by utilizing B-snake in motion compensated and merged feature map with consecutive sequences. The experimental results show the robustness of the proposed algorithm in various road environments. Furthermore, the accuracy of extrinsic camera parameter estimation is evaluated by calculating the distance to a preceding car with the estimated parameters and comparing to the radar-measured distance.

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 novel system for robust lane detection and tracking

This paper presents a lane detection and tracking system based on a novel lane feature extraction approach and the Gaussian Sum Particle filter (GSPF). The proposed feature extraction approach is based on the fact that by zooming into the vanishing point of the lanes, the lane markings/boundaries will only move on the same straight lines they are on. Objects other than the lanes in the frame do not share this property and can be ignored during the model parameter estimation. This algorithm is able to iteratively refine various traditional feature maps and to operate with curved roads. The tracking part of the system is initialised by a deformable template matching algorithm. Three types of tracking algorithms are compared in our study: the original Sequential Importance Resampling (SIR) particle filter, the Gaussian Particle Filter (GPF) and the Gaussian Sum Particles Filter (GSPF). The GSPF achieves the best performance by integrating a novel likelihood function and an intuitive parameter selection process. Both the GSPF and GPF provide improved tracking performance and require less computational power than the SIR. It has also been found that the detection and tracking performance is enhanced significantly by incorporating the refined feature map.

실시간 무인 자동차 제어를 위한 강인한 차선 검출 알고리즘

This paper presents a robust lane detection algorithm based on RGB color and shape information during autonomous car control in realtime. For realtime control, our algorithm increases its processing speed by employing minimal elements. Our algorithm extracts yellow and white pixels by computing the average and standard deviation values calculated from specific regions, and constructs elements based on the extracted pixels. By clustering elements, our algorithm finds the yellow center and white stop lanes on the road. Our algorithm is insensitive to the environment change and its processing speed is realtime-executable. Experimental results demonstrate the feasibility of our algorithm.