Curved Lane Detection Research Articles

Towards Precision Navigation: Curved lane Detection System in Vehicles

Abstract: Curved lane detection is a critical componentof advanced driver assistance systems (ADAS) and autonomous vehicles, playing a pivotal role in ensuring safeand efficient navigation on complex roadways. In the pursuitof enhancing accuracy and expediting processing speed in road lane detection, researchers have proposed numerous methodologies. Despite these efforts, several challenges persist, including variations in lane markings, fluctuations in lighting conditions, and the emergence of shadows. Addressing these obstacles is crucial for establishing dependable lane detection systems. The curve lane detection algorithm exhibits a robust performance, as evidenced by the experimental results, which consistently reveal a notably high success rate in accurately identifying and delineating curved lanes

Safety and Risk Analysis of Autonomous Vehicles Using Computer Vision and Neural Networks

The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability.

An improved and low-complexity neural network model for curved lane detection of autonomous driving system

Lane detection is considered as a key component for the autonomous vehicles of futuristic transport systems. The extraction and fitting problems of lane markers from the road images have been addressed in recent research studies. However, these are still ineffective under curved lane and color light conditions. Illumination changes and the road structure mainly affect the efficiency of lane detection which may lead to traffic accidents especially in case of a curved road. In this work, a novel method based on a low complexity but efficient functional link artificial neural network (FLANN) model is proposed to estimate the entire lane by interpolating the lane markers under different road scenarios. The road image is divided into regions and the extracted lane markers from each region are employed in the proposed trigonometric, polynomial, exponential, and Chebyshev functional expansion based FLANN models for the estimation of the lane curvature. The performance of each model is evaluated and tested on road images using three standard datasets. In terms of mean accuracy and computational time out of four FLANN models, the Chebyshev FLANN (CFLNN) outperforms other three proposed methods. The detection accuracy of CFLANN model is found to be 94.3% which is higher than the results reported by other three models.

Highly Curved Lane Detection Algorithms Based on Kalman Filter

The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

Bayesian curved lane estimation for autonomous driving

Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also does not meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection.

Straight and Curve Lane Detection System for Car Safety using Hough Transform and Sobel Operator

Straight and Curve Lane Detection System for Car Safety using Hough Transform and Sobel Operator

Notice of Retraction: Sharp Curve Lane Detection for Autonomous Driving

Retracted.

Curved Lane Detection using Improved Hough Transform and CLAHE in a Multi-Channel ROI

With the increase in advent of intelligent vehicle technology, LDWS technique is actively developing to provide assistance to drivers in generating warning signal in case of lane departure. In this paper, the input image is divided into four regions of interest. Then the CLAHE is applied on the given ROI for better image contrast and removal of noise using gradient filter. Improved Hough Transform is applied on the given image for the detection of curved and straight lanes. After detecting the lanes, they are colored with green color so that they are clearly visible. The proposed algorithm is analysed based on the parameters like Accuracy, BER, Fmeasure, and Sensitivity. The analysis shows that this algorithm provides efficient and effective performance compared to the existing algorithm.

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.

카메라와 도로평면의 기하관계를 이용한 모델 기반 곡선 차선 검출

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

차선 변화벡터와 카디널 스플라인을 이용한 곡선 차선 검출방법

본 논문에서는 카메라 파라미터가 필요 없는 역 투시변환 영상에 차선 변화벡터와 카디널 스플라인을 이용하여 변화에 강인한 곡선 차선 검출 방법을 제안한다. 이 방법은 역 투시변환과 차선 필터의 전처리 과정이 적용된 영상의 시작 S 프레임과 그 다음 S+1 프레임에서 차선 후보 영역을 설정하여 차선 영역을 검출하고, 검출된 차선영역을 이용하여 차선 변화벡터를 계산한 결과를 가지고 이후의 프레임에서 차선이 위치할 지점을 예측한다. 이후에 예측된 차선 위치에서부터 스캔 영역을 설정하고 이 영역 내에서 새로운 차선 위치를 검출하며, 검출된 차선 위치를 이용해 차선 변화벡터를 갱신하고, 차선 영역 내의 제어 점들에 카디널 스플라인을 적용하여 차선을 검출한다. 제안하는 방법은 차선의 형태 변화에 강인한 곡선 차선 검출방법이지만 직선 차선에도 잘 적응됨을 보였으며 한 프레임을 처리하는 데 약 20ms 정도의 양호한 차선검출 속도를 보였다. The detection method of curves for the lanes which is powerful for the variation by utilizing the lane variation vector and cardinal spline on the inverse perspective transformation screen images which do not required the camera parameters are suggested in this paper. This method detects the lane area by setting the expected lane area in the s frame and next s+1 frame where the inverse perspective transformation and entire process of the lane filter are adapted, and expects the points of lane location in the next frames with the lane variation vector calculation from the detected lane areas. The scan area is set from the nextly expected lane position and new lane positions are detected within these areas, and the lane variation vectors are renewed with the detected lane position and the lanes are detected with application of cardinal spline for the control points inside the lane areas. The suggested method is a powerful method for curved lane detection, but it was adopted to the linear lanes too. It showed an excellent lane detection speed of about 20ms in processing a frame.

Curved lane detection algorithm based on piecewise linear model and heuristic search

Curved lane detection algorithm based on piecewise linear model and heuristic search