A Robust Detection Method for Multilane Lines in Complex Traffic Scenes

As is known to all, there are thousands of people died in traffic accidents in the world every year and 80% of the traffic accidents are due to the drivers' inattention. In order to reduce the occurrence of car accidents, the design of Cognitive Vehicle Safety (CVS) system has attracted wide attention. As an important functional system of the CVS system, Lane Departure Warning system's function is to warn the driver when the vehicle changes the lane without the driver's intention. In this paper, we present a method of lane detection which based on Lane geometric model and image connected region process. The model of the lane is produced by the projection of camera, which makes the parallel lane lines in real world become intersect. The lines on the same side of lane have the same geometric feature that can be used to build lane line geometric model for detection. The lane line can be classified as different lane line models by the geometric feature of it. In this paper, due to the different features of lane line in different images, the operation and the classification of modeling performs at the same time. But before that, we go through some process to pre-process the image, such as the image connected region process, morphology tophat filter, etc. The combination of these modules can overcome the universal lane detection problems, such as the road in different light conditions. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

Lane detection is an essential visual capability for the autonomous vehicle navigation used in the intelligent transportation system (ITS). Several approaches for lane detection have been suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on road marking feature points (RMFP) for autonomous vehicle navigation in an urban environment. The key idea in this paper is to apply methods from extracting RMFP and the target tracking domain to identify lane information. Then the RMFP is extracted from the gray-scale image and the IPM image. Additionally, the lane line color and structure features are used to sift through the RMFP that meet lane lines. Lastly, the clustering method is used to generate lane lines, and these lines are tracked by frame association and Kalman Filter. The experiment results based on image sequences taken under various visual conditions showed that the proposed system can identify the lane line markings with 98% accuracy. The method shows much robustness under various complicated scenarios and meets the real-time requirement.

In intelligent driving, lane line detection is a basic but challenging task, especially in complex road conditions. The current detection algorithms based on convolutional neural networks perform well for simple scenes with plenty of light, and the lane lines are clean and unobstructed. Still, they do not perform well for complex scenes such as damaged, blocked, and lack-of-light scenes. In this article, we have exceeded the above restrictions and propose an attractive network: LaneFormer; We use an end-to-end network for up and down sampling three times each, then fuse them in their respective channels to extract the slender lane line structure. At the same time, a correction module is designed to adjust the dimensions of the extracted features using MLP, judging whether the feature is completely extracted through the loss function. Finally, we send the feature into the transformer network, detect the lane line points through the attention mechanism, and design a road and camera model to fit the identified lane line feature points. Our proposed method has been validated in the TuSimple benchmark test, showing the most advanced accuracy with the lightest model and fastest speed.

<div class="section abstract"><div class="htmlview paragraph">Accurately detecting lane lines remains a challenging task, especially with low-quality cameras due to the complex environment such as haze, uneven lighting, and shadows of actual roads. Despite numerous studies, lane line detection algorithms are still required to be improved for practical applications. In this work, we propose a new lane detection method that incorporates the brightness estimation concept of the single-scale retinex (SSR) algorithm into the dark channel prior (DCP) algorithm for image preprocessing. The improved DCP algorithm is used to estimate the atmospheric light intensity and remove haze noise, while simultaneously enhancing image contrast to reduce the difficulty of lane detection, especially under uneven lighting conditions, then followed by perspective transformation and HSV color space (hue, saturation, value) conversion, and finally, lane line recognition and tracking are performed using sliding windows and histogram statistics. Experimental results demonstrate that our method achieves high accuracy and robustness in lane line detection, especially under uneven lighting conditions, compared to existing methods. The proposed method can effectively handle complex situations such as haze, uneven lighting, and shadows, making it feasible for practical applications.</div></div>

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%.

<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>

CFI-Former: Efficient lane detection by multi-granularity perceptual query attention transformer.

In order to adapt to various complex lane environments, a general fast lane extraction algorithm was proposed. Firstly, according to the mutability of the local gray value and the characteristics of image saliency, the collected road image was preprocessed, and the region of interest was obtained by the proposed double threshold algorithm. Only in the significant region of the road were the image gray value change and image smoothing carried out to solve the problems of consuming too much time and poor noise resistance in lane detection. The lane line edge was then extracted based on the improved Canny operator. When the Otsu threshold was selected, the Kalman filter algorithm was introduced to quickly predict the optimal threshold of the subsequent image sequence in accordance with the characteristics of optimized autoregressive data processing.. Finally, for the straight line fitted by the Hough transform, an effective multi-layer evaluation function was established to realize the online correction of lane lines. Compared with the traditional lane line extraction algorithm, the experimental results show that the proposed algorithm has better accuracy, real-time performance and robustness.

Lane detection plays an important role in the field of automatic driving. Conventional convolutional operations typically focus on local block-like region, while lane often span across multiple image regions as strip lines. How to make up for the limitations of ordinary convolution in extracting lane information and make the network focus on the strip lanes is a challenging problem. For this purpose, we propose an oblique convolution idea to effectively extract the information of the whole lane. Specifically, an Oblique Rotation Module (ORM) is proposed to rotate the lane feature map into a vertical orientation, enabling better lane positioning and recognition by using row anchors. Additionally, we introduce a Strip Spatial Attention Module (SSAM) to extract global features of the entire lane and employ deformable convolutions to adapt to curved lane lines. Finally, we restore the feature map using Anti-oblique Rotation Module (AORM) and obtain lane detection results through classification-based predictions. During training, we incorporate a multi-scale auxiliary classification loss to predict the presence of lane lines at different network levels, aiding the network in learning the structural information of lane lines. A large number of experiments on the lane detection benchmark dataset show that our method can achieve advanced performance, and we have achieved good results in public datasets. The accuracy on the Tusimple dataset is 96.50%, the F1 on the CULane dataset is 79.33%. Both results rank in the top 10 on the leaderboard, and the inference speed reaches 35 FPS. Code and data are available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gongyan1/Oblique-Convolution</uri>

Robust lane line segmentation based on group feature enhancement

Lane detection is one of the most fundamental problems in the rapidly developing field of autonomous vehicles. With the dramatic growth of deep learning in recent years, many models have achieved a high accuracy for this task. However, most existing deep-learning methods for lane detection face two main problems. First, most early studies usually follow a segmentation approach, which requires much post-processing to extract the necessary geometric information about the lane lines. Second, many models fail to reach real-time speed due to the high complexity of model architecture. To offer a solution to these problems, this paper proposes a lightweight convolutional neural network that requires only two small arrays for minimum post-processing, instead of segmentation maps for the task of lane detection. This proposed network utilizes a simple lane representation format for its output. The proposed model can achieve 93.53% accuracy on the TuSimple dataset. A hardware accelerator is proposed and implemented on the Virtex-7 VC707 FPGA platform to optimize processing time and power consumption. Several techniques, including data quantization to reduce data width down to 8-bit, exploring various loop-unrolling strategies for different convolution layers, and pipelined computation across layers, are optimized in the proposed hardware accelerator architecture. This implementation can process at 640 FPS while consuming only 10.309 W, equating to a computation throughput of 345.6 GOPS and energy efficiency of 33.52 GOPS/W.

LaneScanNET: A deep-learning approach for simultaneous detection of obstacle-lane states for autonomous driving systems

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

Lane departure warning system (LDWS) has been regarded as an efficient method to lessen the damages of road traffic accident resulting from driver fatigue or inattention. Lane detection is one of the key techniques for LDWS. To overcome the contradiction between complexity of algorithm and the real‐time requirement for vehicle onboard system, this paper introduces a new lane detection method based on intelligent CCD parameters regulation. In order to improve the real‐time capability of the system, a CCD parameters regulating method is proposed which enhances the contrast between lane line and road surfaces and reduces image noise, so it lays a good foundation for the following lane detection. Hough transform algorithm is improved by selection and classification of seed points. Finally the lane line is extracted through some restrictions. Experimental results verify the effectiveness of the proposed method, which improves not only real‐time capability but also the accuracy of the system.

Lane line is a very important element in HD maps, and map updating based on information can effectively reduce production cost. We use the images obtained by crowdsourcing for information mining. Most of these images are discontinuous and there are no internal or external parameters. However, lane detection algorithms are mostly applied to the vehicle, which are not suitable to detect road changed information. We propose a lane line perception network for information discovery, which directly takes the returned image as input and outputs the number of lane lines, as well as the color and type attributes of each lane. In contrast to previous works, we have solved the gradient explosion problem and specially optimized type segmentation. Finally, the proposed method is applied to mine information about lane changes.