A Fast and Accurate Lane Detection Method Based on Row Anchor and Transformer Structure

A robust and real-time lane detection method in low-light scenarios to advanced driver assistance systems

An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

Enhanced Cross Layer Refinement Network for robust lane detection across diverse lighting and road conditions

<div class="section abstract"><div class="htmlview paragraph">Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical <i>R</i><sup>2</sup> score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.</div></div>

Lane marking detection is challenging under adverse weather, especially in rain. Rain affects the visibility by changing the amount of light reflected from the road and splashing of rain periodically blocks vision through the car windshield. Colored (yellow, blue etc.) and eroded lane markings have lower contrast between lane and road surface. Rain affects the visibility of these colored and eroded lanes severely. In this paper, a robust lane detection and tracking method is proposed to detect lane under heavy rainfall condition. Here, an automatic adjustable threshold (AAT) is proposed, which improves the performance of canny edge detector to detect colored, eroded and blurred lane markings. Probabilistic Hough Transform (PHT) is used to detect candidate lane lines. A lane verification technique slope and angle based geometric constraints (SAGC) is used to detect and verify lane markings. Finally, a lane tracking technique by angle range validation (ARV) is introduced, which can keep track of the lane position when either left or right or both lane markings are partially or fully invisible due to erosion or occlusion or when lanes are not even painted on the road. Experimental results show that the average detection rate is 94.12%, and the average detection time is 30.24ms per frame which outperformed state-of-the-art method.

Lane detection is crucial for driver assistance systems. However, road scenes are severely degraded in dense fog, which leads to the loss of robustness of many lane detection methods. For this problem, an end-to-end method combining polarimetric dehazing and lane detection is proposed in this paper. From images with dense fog captured by a vehicle-mounted monochrome polarization camera, the darkest and brightest images are synthesized. Then, the airlight degree of polarization is estimated from angle of polarization, and the airlight is optimized by guided filtering to facilitate lane detection. After dehazing, the lane detection is carried out by a Canny operator and Hough transform. Having helped achieve good lane detection results in dense fog, the proposed dehazing method is also adaptive and computationally efficient. In general, this paper provides a valuable reference for driving safety in dense fog.

Lane detection is the key technology of an advanced driver assistance system. This study proposed an efficient and novel lane detection method, Lane Kernel Parameter Module ResNet-18 (LKPM-ResNet-18), to improve the detection efficiency of lane with complex topologies (such as divergent lines and dense lines). This method can precisely predict the global structure in a bottom-up manner and directly output the curve parameters of the lane alignment model. In addition, LKPM was used to obtain accurately the local structure information and the slender shape of the lane with complex topology, and then the shape convolution kernel parameters of each instance are dynamically predicted to optimize the results of the subsequent output curves. Finally, the prior information of the global lane is fully integrated through the lane structure constraint loss, and the optimization results are fused into the global geometric curve fitting output, so that the fitted lanes are smoother and more continuous. The method proposed has real-time detection efficiency and requires little postprocessing because of the end-to-end direct approach. The validated results show that the efficiency of this method is obviously better than that of State-Of-The-Art (SOTA) methods, such as PointLaneNet and ENet-SAD. The F1 scores increase by 0.43% and 4.92%, respectively compared with the latest CurveLanes-NAS-L and SCNN on the CULane test dataset. The accuracy of 88.98% can be obtained on the CurveLanes test dataset. Meanwhile, the result from real driving environment detection exceeds the top performances of the other by 2.22% with the accuracy of 86.47%.

Lane detection and tracking technique is the autonomous driving basis for Rubber-Tired Gantries (RTGs), vital to the automation and intelligence updating of man-driven container terminals. However, the existing lane detection methods developed for common road scenarios cannot meet the high-precision and robust all-weather requirements of RTG autonomous driving. In this article, we propose an Adaptive Edge-based Lane Detection and Tracking method considering RTG lanes’ characteristics in this paper. First, the candidate edges of lane lines are detected and paired based on the enhanced gradient features. Next, inverse perspective mapping is employed to search the right edges, followed by an adaptive sliding-window method. Ultimately, we develop an adaptive Kalman filter to track lane lines robustly, detecting confidence weighting by relaxing the constraint of lane line width. The proposed method is tested in an actual container yard, the lane centerline’s average position error is 2.051 pixels, and the detection success rate is close to 100%.

Dealing with complex and variable road scenes is a challenge for autonomous driving, so lane detection with good performance is crucial. Currently, mainstream lane detection methods are divided into semantic segmentation-based and instance segmentation-based methods. The former is limited to detect a pre-defined, fixed number of lanes and unable to cope with lane changes, while the latter requires post-clustering processing despite a variable number of lanes detected. In this paper, we propose a novel instance segmentation-based method using lane locations to get an arbitrary number of lane instances directly. In addition, in order to improve the speed of lane detection, we employee key points to represent lanes instead of pixels to reduce the granularity of segmentation. In conclusion, we propose a real-time lane detection method which can predict an arbitrary number of lanes directly. We validate our method on two public datasets and the competitive results are obtained by comparing with existing methods.

Mobile robots is getting popular with the public areas, such as airport, train station, etc. Among the technical methods for mobile robotic system, lane detection is an essential part, because lanes provide information for the robot system to keep current lane or switch to another lane space. However, existing lane detection algorithms are based on the assumption that the lane markings have good quality, but sometimes the lane markings are non-continuous and poor. In this paper, we propose a real-time multi-lane detection algorithm that is robust to the challenging lane condition. For basic lane detection part, we extract the lane features from images, and based on the features geometric uncertainty algorithm we can detect the lanes, guardrails, other vehicles in real-time. What’ s more, the accuracy can be improved with the other vehicles direction information and guardrail depth information from stereo vision when the lane features are non-continuous or poor. Next, to prevent the false detection, an improved RANSAC (random sample consensus) algorithm is introduced in the algorithm. Finally, the proposed algorithm is evaluated on our dataset with different lane conditions. Experimental results show good robustness on variation of the lane conditions.

As the high growth of the number of vehicles, the traffic accidents are becoming more and more serious in recent years. In order to avoid the drivers being in danger, an intelligent vision-based system should focus on the image contents of the front the camera setting under the rear-view mirror in the vehicle. In this paper, we present a functional-link-based neuro-fuzzy network (FLNFN) structure for lane detection and departure warning system application. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. The lane detection method and the departure warning system proposed in this paper have been successfully evaluated on a PC platform of 3.2-GHz CPU, where the average frame-rate is up to 30fps.

Dynamically Enhanced lane detection with multi-scale semantic feature fusion

Advanced Driver Assistance systems (ADAS) have seen increasing popularity due to their importance in automotive driver safety and autonomous driving. These systems analyze data from several sensors mounted on the vehicle to detect lanes, obstacles and traffic conditions to ensure safe driving. Lane markings on road with different color and structure provide information on safe drive zone and other traffic restrictions on road. Typical ADAS solutions depend on vision based sensors for lane detection. Here we propose an efficient algorithm for detecting type and color of the lane marks as this information plays critical role in taking the decision for safety features such as lane change and lane keep assist. Our algorithm is pluggable to any state-of-art lane detection algorithm and provides lane type and color for straight, curvy roads. The proposed method is tested on various challenging scenarios and results are promising.

Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle’s departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.

<div class="section abstract"><div class="htmlview paragraph">The camera-based advanced driver assistance systems (ADAS) like lane departure warning system (LDWS) and lane keeping assist (LKA) can make vehicles safer and driving easier. Lane detection is indispensable for these lane-based systems for achieving vehicle local localization and behavior prediction. Since the vision is vulnerable to the variable environment conditions such as bad weather, occlusions and illumination, the robustness is important. In this paper, a robust algorithm for detecting and tracking multiple lanes with arbitrary shape is proposed. We extend the previously lane detection and tracking process from the space domain to the temporal-spatial domain by using a more robust and general multi-lane model. First, new slice images containing temporal information are generated from image sequences. Instead of binarization process, we use a more general detector for extracting the lane marker candidates with prior knowledge to generate the binary slice image. Then, all the lane marker candidates are clustered into many lanes and as the initialization of the following particle filtering tracking process. We calculate the distance map in the binary slice image by using a modified distance algorithm and sample particles in the distance map. Finally, we can track the lane markers with particle filtering successfully. A range of experiment results indicate that the proposed algorithm can detect and track multiple lanes correctly and robustly. Comparing with other method, it has enough tolerance to variant illumination and occlusion.</div></div>