Lane Detection Method Research Articles

Robust Accurate Lane Detection and Tracking for Automated Rubber-Tired Gantries in a Container Terminal

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%.

MultiNet-GS: Structured Road Perception Model Based on Multi-Task Convolutional Neural Network

In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection model, the YOLOv8 model, as the encoder structure of our model. We introduce a new dynamic sparse attention mechanism, BiFormer, in the feature extraction part of the model to achieve more flexible computing resource allocation, which can significantly improve the computational efficiency and occupy a small computational overhead. We introduce a lightweight convolution, GSConv, in the feature fusion part of the network, which is used to build the neck part into a new slim-neck structure so as to reduce the computational complexity and inference time of the detector. We also add an additional detector for tiny objects to the conventional three-head detector structure. Finally, we introduce a lane detection method based on guide lines in the lane detection part, which can aggregate the lane feature information into multiple key points, obtain the lane heat map response through conditional convolution, and then describe the lane line through the adaptive decoder, which effectively makes up for the shortcomings of the traditional lane detection method. Our comparative experiments on the BDD100K dataset on the embedded platform NVIDIA Jetson TX2 show that compared with SOTA(YOLOPv2), the mAP@0.5 of the model in traffic object detection reaches 82.1%, which is increased by 2.7%. The accuracy of the model in drivable area detection reaches 93.2%, which is increased by 0.5%. The accuracy of the model in lane detection reaches 85.7%, which is increased by 4.3%. The Params and FLOPs of the model reach 47.5 M and 117.5, which are reduced by 6.6 M and 8.3, respectively. The model achieves 72 FPS, which is increased by 5. Our MultiNet-GS model has the highest detection accuracy among the current mainstream models while maintaining a good detection speed and has certain superiority.

Multitask Knowledge Distillation Guides End-to-End Lane Detection

Autonomous driving has witnessed rapid development with the application of artificial intelligence technology in recent years. Lane detection is one of the tasks of environment perception, which affects the planning and decision-making directly, and requires the algorithm to meet both high precision and high efficiency. Most of the existing methods extract pixels belonging to lanes in the image, which should be post-processed, otherwise it cannot be applied to subsequent tasks like planning. This paper proposes an end-to-end lane detection method that utilizes auxiliary supervision and knowledge distillation based teaching-test module to predict the parameters of polynomials of lanes directly. The teaching-test module guides the polynomial regression branch to learn the shape features from the segmentation branch to improve the fitting accuracy under complex road conditions. The proposed method is validated on TuSimple and CULane datasets, and is competitive with state-of-the-art methods in efficiency and accuracy.

Enhancing Lane-Tracking Performance in Challenging Driving Environments through Parameter Optimization and a Restriction System

The autonomous driving market has experienced rapid growth in recent times. From systems that assist drivers in keeping within their lanes to systems that recognize obstacles using sensors and then handle those obstacles, there are various types of systems in autonomous driving. The sensors used in autonomous driving systems include infrared detection devices, lidar, ultrasonic sensors, and cameras. Among these sensors, cameras are widely used. This paper proposes a method for stable lane detection from images captured by camera sensors in diverse environments. First, the system utilizes a bilateral filter and multiscale retinex (MSR) with experimentally optimized set parameters to suppress image noise while increasing contrast. Subsequently, the Canny edge detector is employed to detect the edges of the lane candidates, followed by utilizing the Hough transform to make straight lines from the land candidate images. Then, using a proposed restriction system, only the two lines that the current vehicle is actively driving within are detected from the candidate lines. Furthermore, the lane position information from the previous frame is combined with the lane information from the current frame to correct the current lane position. The Kalman filter is then used to predict the lane position in the next frame. The proposed lane-detection method was evaluated in various scenarios, including rainy conditions, low-light nighttime environments with minimal street lighting, scenarios with interfering guidelines within the lane area, and scenarios with significant noise caused by water droplets on the camera. Both qualitative and quantitative experimental results demonstrate that the lane-detection method presented in this paper effectively suppresses noise and accurately detects the two active lanes during driving.

A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

A Study on Vision Based Lane Detection Methods for Advanced Driver Assistance Systems

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.

Lane Detection Methods Survey for Automatic Driving

In realizing automatic driving, environment perception is an important task, and the research of lane detection algorithms occupies the leading position in environment perception. After many years of research on lane detection by researchers, many effective lane detection methods are now available. This article will research and analyze mainstream lane detection methods, including traditional methods and learning-based methods. Later, through an in-depth analysis of these methods, the advantages and disadvantages of each method are evaluated, and the direction of lane detection is discussed. It is hoped to help choose suitable lane detection methods in different scenes.

Implementing Model Predictive Control and Steady-State Dynamics for Lane Detection for Automated Vehicles in a Variety of Occlusion in Clothoid-Form Roads.

Lane detection in driving situations is a critical module for advanced driver assistance systems (ADASs) and automated cars. Many advanced lane detection algorithms have been presented in recent years. However, most approaches rely on recognising the lane from a single or several images, which often results in poor performance when dealing with extreme scenarios such as intense shadow, severe mark degradation, severe vehicle occlusion, and so on. This paper proposes an integration of steady-state dynamic equations and Model Predictive Control-Preview Capability (MPC-PC) strategy to find key parameters of the lane detection algorithm for automated cars while driving on clothoid-form roads (structured and unstructured roads) to tackle issues such as the poor detection accuracy of lane identification and tracking in occlusion (e.g., rain) and different light conditions (e.g., night vs. daytime). First, the MPC preview capability plan is designed and applied in order to maintain the vehicle on the target lane. Second, as an input to the lane detection method, the key parameters such as yaw angle, sideslip, and steering angle are calculated using a steady-state dynamic and motion equations. The developed algorithm is tested with a primary (own dataset) and a secondary dataset (publicly available dataset) in a simulation environment. With our proposed approach, the mean detection accuracy varies from 98.7% to 99%, and the detection time ranges from 20 to 22 ms under various driving circumstances. Comparison of our proposed algorithm's performance with other existing approaches shows that the proposed algorithm has good comprehensive recognition performance in the different dataset, thus indicating desirable accuracy and adaptability. The suggested approach will help advance intelligent-vehicle lane identification and tracking and help to increase intelligent-vehicle driving safety.

Lane Detection Method under Low-Light Conditions Combining Feature Aggregation and Light Style Transfer

Lane Detection Method under Low-Light Conditions Combining Feature Aggregation and Light Style Transfer

Ego Vehicle Lane Detection and Key Point Determination Using Deep Convolutional Neural Networks and Inverse Projection Mapping

Abstract Ego lane detection is one of the key techniques in Ego Lane Analysis System (ELAS) implemented in smart autonomous driving cars for lane detection in roads. This technique has been extensively studied in recent years because of its accurate and robust detection of shape and location of lanes. The conventional methods are less robust and computationally expensive since they have several challenges in localization of lanes due to presence of occlusions on roads. So to avoid these issues, this paper uses a novel 2-stage lane detection method using deep convolutional neural network to detect the lanes and its key-points by optimally fit a curve to the lane to compensate on above mentioned issues. The proposed methodology for lane detection predicts the key-points accurately and it robust under various weather conditions and highway driving scenarios. In terms of performance, this technique is fast and robust with low computational cost and has high performance when deployed on autonomous vehicle-based systems.

Advanced Driver Assistance System

bstract: Many people die each year in roadway departure crashes caused by driver inattention. Lane detection systems are useful in avoiding these accidents as safety is the main purpose of these systems. Such systems have the goal to detect the lane marks and to warn the driver in case the vehicle tends to depart from the lane. A lane detection system is an important element of many intelligent transport systems. Lane detection is a challenging task because of the varying road conditions that one can come across while driving. In the past few years, numerous approaches for lane detection were proposed and successfully demonstrated. In this paper, a comprehensive review of the literature in lane detection techniques is presented. The main objective of this paper is to discover the limitations of the existing lane detection methods.

Fast lane detection based on sparse prediction

Lane detection plays an important role in driving safety, so it is necessary to explore more practical lane detection methods. Nowadays, with the development of deep learning, mainstream methods regard the lane detection task as a pixel-level image segmentation task. Although the powerful neural network can greatly improve the detection accuracy, it also brings a large computational cost and reduces the running speed. Therefore, achieving high-precision and high-speed lane detection is still challenging. In this paper, we design an efficient and fast sparse prediction method. Specifically, our method describes the lane line by predicting the location of discrete lane points, so the calculation magnitude is the number of discrete points. This method greatly reduces the calculation cost and is not limited by the number of lane lines. The network structure uses an Encoder–Decoder architecture, and the large receptive field obtained by the structure after downsampling can maintain the spatial continuity between lane points, thus ensuring high accuracy. In addition, self-attention distillation is used to improve the learning ability of the network. Compared with other state-of-the-art methods on the TuSimple and CULane datasets, sparse prediction method achieves high accuracy with obvious speed advantage.

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.

MT-Net: Fast video instance lane detection based on space time memory and template matching

Currently, only a few lane detection methods focus on the dynamic characteristics of a video. In continuous prediction, single-frame detection results produce different degrees of jitter, resulting in poor robustness. We propose a new fast video instance lane detection network, called MT-Net, based on space–time memory and template matching. Memory templates were used to establish feature associations between past and current frames from a local–global perspective to mitigate jitter from scene changes and other disturbances. Moreover, we also investigated the sources and spreading mechanism of memory errors. We designed new query frame and memory encoders to obtain higher-precision memory and query frame features. The experimental results showed that, compared with state-of-the-art models, the proposed model can reduce the number of parameters by 62.28% and the unnecessary jitter and unstable factors in muti-frame lane prediction results by 12.70%, and increases the muti-frame lane detection speed by 1.79. Our proposed methods has obvious advantages in maintaining multi-frame instance lane stability and reducing errors.

Lane Detection in Autonomous Vehicles: A Systematic Review

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle’s intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

Review on lane detection and related methods

Road detection remains a captivating and crucial aspect of any form of autonomous driving. In this manuscript, we furnish a comprehensive appraisal of recent advancements in road lane detection, a fundamental component integral to autonomous driving. Despite numerous methodologies being proposed to augment accuracy while expediting speed, various hindrances, including lane marking variations, lighting fluctuations, and shadowy conditions, necessitate the establishment of dependable detection systems. Model-based and learning-based methods represent the two predominant techniques for lane detection. Model-based methods afford rapid computation speeds, while learning-based methods extend robustness amidst complexity. This paper delves into the techniques of lane detection and forecasts upcoming trends in the field. Collectively, this review offers a sturdy foundation for prospective research in the realm of road lane detection.

Learning Light Fields for Improved Lane Detection

Robust lane detection is imperative for the realization of intelligent transportation. Recently, vision-based systems that employ deep convolution neural networks (CNNs) for lane detection have made considerable progress. However, for better generalization under various road conditions learning-based methods require excessive training data, which becomes non-trivial in challenging conditions such as illumination variation, shadows, false lane lines, and worn lane markings, etc. In this paper, we propose a light field (LF) based lane detection method that utilizes the additional angular information for improved prediction and increased robustness. Two different LF representations are investigated to study the possibility of maximum performance improvement and minimal additional computation cost and data labeling efforts. Experimental results successfully demonstrate that the proposed approach improves the prediction of the lane line point coordinates and is significantly robust against the aforementioned adverse conditions.

Research on Lane Detection Method Based on Machine Vision

Research on Lane Detection Method Based on Machine Vision

Lane detection method based on improved Hough transform

Lane detection method based on improved Hough transform

Adaptive Multi-Lane Detection Based on Robust Instance Segmentation for Intelligent Vehicles

Recent advances in lane detection based on deep neural networks have enhanced the detection performance of intelligent vehicles under different traffic scenes. However, it’s still difficult for existing lane detection algorithms to robustly extract lane instances and adapt to varying lane numbers simultaneously. In this paper, we focus on anchor-free methods and propose an adaptive multi-lane detection approach based on instance segmentation for intelligent vehicles. By combining instance segmentation with lane center estimation, the proposed approach achieves robust and efficient performance. Besides, cosine-metric is incorporated into the objective functions that enables the proposed method to extract more discriminative features for foreground extraction. Different from the proposed approach, most existing algorithms treat the lane instance detection task as a multi-class object detection problem which require to predefine different lane categories and fix the lane number in advance, while the lane category predefinition degrades the robustness of the algorithms. Experimental results on public datasets demonstrate that the proposed approach achieves better overall performance compared with the state-of-the-art anchor-free lane detection methods. It is also illustrated that the proposed approach can be easily extended to other instance segmentation tasks, e.g. vehicle segmentation.