Lane Features Research Articles

Computer vision-based model for detecting turning lane features on Florida’s public roadways from aerial images

ABSTRACT Efficient collection of roadway geometry data is crucial for effective transportation planning, maintenance, and design. Current methods involve land-based techniques like field inventory and aerial-based methods such as satellite imagery. However, land-based approaches are labor-intensive and costly, prompting the need for more efficient methodologies. Consequently, there exists a pressing need to develop more efficient methodologies for acquiring this data promptly, safely, and economically. This study proposes a computer vision-based approach to detect turning lane markings from aerial images in Florida. The method aims to identify aged or faded markings, compare lane locations with other features, and analyze intersection crashes. Validation in Leon County achieved 80.4% accuracy, detecting over 13,800 turning lane features in Duval County, Florida. This data integration offers valuable insights for policymakers and road users, highlighting the significance of automated extraction methods in transportation planning and safety.

LHFFNet: A hybrid feature fusion method for lane detection

Lane line images have the essential attribute of large-scale variation and complex scene information, and the similarity between adjacent lane lines is high, which can easily cause classification errors. And remote lane lines are difficult to recognize due to visual angle changes in width. To address this issue, this paper proposes an effective lane detection framework, which is a hybrid feature fusion network that enhances multiple spatial features and distinguishes key features throughout the entire lane line segment. It enhances and fuses lane line features at multiscale to enhance the feature representation of lane line images, especially at the far end. Firstly, in order to enhance the correlation of multiscale lane features, a multi-head self attention is used to construct a multi-space attention enhancement module for feature enhancement in multispace. Secondly, a spatial separable convolutional branch is designed for the jumping layer structure connecting multiscale lane line features. While retaining feature information of different scales, important lane areas in multiscale feature information are emphasized through the allocation of spatial attention weights. Finally, considering that lane lines are elongated areas in the image, and the background information in the image is much more abundant than lane line information, the flexibility of traditional pooling operations in capturing widely existing anisotropic contexts in actual environments is limited. Therefore, before embedding feature output branches, strip pooling is introduced to refine the representation of lane line information and optimize model performance. The experimental results show that the accuracy on the TuSimple dataset reaches 96.84%, and the F1 score on the CULane dataset reaches 75.9%.

Turning Features Detection from Aerial Images: Model Development and Application on Florida’s Public Roadways

Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.

Multimodal Fusion Network for 3-D Lane Detection.

3-D lane detection is a challenging task due to the diversity of lanes, occlusion, dazzle light, and so on. Traditional methods usually use highly specialized handcrafted features and carefully designed postprocessing to detect them. However, these methods are based on strong assumptions and single modal so that they are easily scalable and have poor performance. In this article, a multimodal fusion network (MFNet) is proposed through using multihead nonlocal attention and feature pyramid for 3-D lane detection. It includes three parts: multihead deformable transformation (MDT) module, multidirectional attention feature pyramid fusion (MA-FPF) module, and top-view lane prediction (TLP) ones. First, MDT is presented to learn and mine multimodal features from RGB images, depth maps, and point cloud data (PCD) for achieving optimal lane feature extraction. Then, MA-FPF is designed to fuse multiscale features for presenting the vanish of lane features as the network deepens. Finally, TLP is developed to estimate 3-D lanes and predict their position. Experimental results on the 3-D lane synthetic and ONCE-3DLanes datasets demonstrate that the performance of the proposed MFNet outperforms the state-of-the-art methods in both qualitative and quantitative analyses and visual comparisons.

Lane detection using hough transformation and Yolov8

Autonomous vehicles necessitate the integration of advanced technologies such as computer vision and deep learning to comprehend and navigate their surroundings. A crucial yet challenging component of this integration is the accurate detection of lanes, which can be influenced by a multitude of varying lane characteristics and conditions. This research undertakes a comparative analysis of lane detection methodologies, explicitly focusing on traditional image processing techniques and Convolutional Neural Networks (CNNs). The evaluation utilized a sample of 500 images from the CULane dataset, which encompasses a diverse range of traffic scenarios. Initially, a method incorporating Gaussian blurring, Canny edge detection, and Hough line transformation was examined. Despite its efficiency, operating at 30 frames per second, this approach exhibited a high error rate (average Mean Squared Error (MSE) of 0.537), which is attributable to the loss of critical image details during the preprocessing stage. Subsequently, the performance of a fine-tuned YOLOv8 model, trained on a reformatted version of the CULane dataset was assessed. The combination of object detection and subsequent Hough transformation yielded high accuracy, demonstrating the model’s ability to learn and identify relevant lane features. The deep CNNs demonstrated superior performance over classical image processing techniques in terms of lane detection accuracy, thereby underscoring their potential applicability within the realm of autonomous vehicle technology

Robust Lane Detection Method Based on Dynamic Region of Interest

Lane detection is an important component of vehicle assisted driving systems. Many lane line algorithms have been proposed, but lane line detection is still a challenging task in environments with road text or vehicle interference. To address these issues, this paper proposes a robust lane line detection method under structured roads. First, we propose a method based on the slope and length of a straight line and the distance of the line from the centre line to extract regions of interest reducing interference from other invalid regions. Afterwards, we use a new fusion method to fuse the extracted colour features with the gradient features, enhancing the lane features while greatly reducing the interference in the pixels. Next, we cut the extracted pixel feature maps in equal parts into several sub-maps and perform pixel statistics for each sub-map, replacing sub-maps with abnormal pixel values with adjacent sub-maps, which can effectively reduce noise. Finally, we use DBSCAN to cluster the pixels and fit the lane lines with least squares. The experimental results show that the method can effectively detect lane lines and has good robustness.

A LANE TRACKING ALGORITHM FOR LOW-COMPUTATIONAL-POWER MICROCONTROLLER-CONTROLLED AUTONOMOUS VEHICLE MODELS

At work, three tasks were presented: road lane detection and trajectory estimation, environment mapping, and the application of a neural network. All these tasks are based on the results of the lane detection method. The presented lane detection method stands out due to the execution of an interpolation transformation for all previously detected edge points. This transformation transfers these points to a “bird’s-eye” coordinate system and distributes them on a grid. Road lanes are identified by a lane feature filter based on the analysis of the distances between unique points. This allows lane views to be obtained in a coordinate system while preserving the distance condition. The road environment map is constructed from the obtained images using a probabilistic algorithm called Distributed Particle-SLAM (DP-SLAM). Based on the map result, a method for representing characteristic points describing the path of road lanes in each incoming camera image has been developed. These points are then used for training the neural network. The neural network solves a regression task for the coordinates of the points on the road lanes, enabling the identification of coefficients for parabolic fitting. Validation has been performed.

Sketch and Refine: Towards Fast and Accurate Lane Detection

Lane detection is to determine the precise location and shape of lanes on the road. Despite efforts made by current methods, it remains a challenging task due to the complexity of real-world scenarios. Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently. Proposal-based methods detect lanes by distinguishing and regressing a collection of proposals in a streamlined top-down way, yet lack sufficient flexibility in lane representation. Keypoint-based methods, on the other hand, construct lanes flexibly from local descriptors, which typically entail complicated post-processing. In this paper, we present a “Sketch-and-Refine” paradigm that utilizes the merits of both keypoint-based and proposal-based methods. The motivation is that local directions of lanes are semantically simple and clear. At the “Sketch” stage, local directions of keypoints can be easily estimated by fast convolutional layers. Then we can build a set of lane proposals accordingly with moderate accuracy. At the “Refine” stage, we further optimize these proposals via a novel Lane Segment Association Module (LSAM), which allows adaptive lane segment adjustment. Last but not least, we propose multi-level feature integration to enrich lane feature representations more efficiently. Based on the proposed “Sketch-and-Refine” paradigm, we propose a fast yet effective lane detector dubbed “SRLane”. Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9%. The source code is available at: https://github.com/passerer/SRLane.

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network.

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.

The lane detection algorithm based on multiscale aggregated attention fusion network

High accuracy and high stability are key elements of the lane detection algorithm in an autonomous driving system. Traditional algorithms are having difficulty extracting detailed features due to the complex geometric structure and background interference of lanes in real scenarios. Therefore, this paper proposes a Multiscale Aggregated Attention Fusion (MAAF) network, which integrates attention mechanisms to improve the accuracy and robustness of lane detection. Firstly, the Recurrent Feature-Shift Aggregator for Lane Detection (RESA) is improved to increase the effective sensory field and improve the efficiency of feature aggregation. Then, the ECANet attention module is used to extract features across channels, enhancing the model's focus on lane details. Finally, a spatial attention mechanism is incorporated to make the network more attentive to lane features, acquire more semantic information, and reduce the influence of background interference and clutter. Experimental results show that this method achieves 96.84% and 76.5% metrics on the TuSimple and CuLane datasets, respectively, surpassing the baseline network. Furthermore, it demonstrates good generalization and robustness, enabling accurate lane detection in complex road environments.

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.

Examining the Safety Impacts of High-Occupancy Vehicle Lanes: International Experience and an Evaluation of First Operation in Israel

Current transport policies promote better use of existing roadways by using traffic management strategies such as high-occupancy vehicle (HOV) lanes. International experience showed positive mobility impacts of HOV lanes, while research evidence on their safety implications is limited. In Israel, the first HOV lanes were introduced in 2019. This study examined the impacts of HOV lanes on road safety based on a detailed review of international research and accident analyses, which evaluated the safety effects of HOV lanes in Israel. The literature survey applied a systematic screening of research studies from the past two decades and found that HOV lanes were frequently associated with an adverse effect on road safety. Yet, findings were limited to the North American experience, with mostly left-side HOV lanes in use, while in Israel, right-side HOV lanes were introduced. In Israeli evaluations, before-after comparisons of accident changes with comparison groups were applied, with regression models fitted to monthly time series of 17 accident types. Results showed that HOV lanes’ operation led to increasing accident trends, particularly in interchange areas and in the daytime. In injury accidents on road sections, an average increase of 31–41% was found (yet non-significant), while at interchange areas, an increase was even higher and sometimes significant. Thus, adverse safety effects should be expected and accounted for in future planning of HOV lanes. Further research should explore the design features of HOV lanes to reduce their negative safety implications.

A multi-modal vehicle trajectory prediction framework via conditional diffusion model: A coarse-to-fine approach

Accurate prediction of the future motion of surrounding vehicles is crucial for ensuring the safety of motion planning in autonomous vehicles. However, it is challenging to perform because of the complex interactions between vehicles. In this study, we propose a novel multi-modal vehicle trajectory prediction framework that utilizes a coarse-to-fine approach by first generating initial trajectory proposals with a conditional variational autoencoder (CVAE) and then refining them using the conditional diffusion model. We first address the problems of data sparsity and irregularity by converting the trajectory coordinates to the Frenet coordinate system. To enable the model to better distinguish between different features, we employ a temporal encoder to extract trajectory features and a long short-term memory (LSTM) network to extract lane features. The target lane evaluator is utilized to calculate the attention weights for each lane candidate, thereby generating more deterministic future trajectories. We then use the CVAE to generate initial multiple trajectory proposals based on the surrounding scene context and the trajectory features of the target vehicle. Finally, we formulate the trajectory refinement task as a reverse process of the conditional diffusion model, which effectively enhances the multi-modal trajectory proposals. Experiments conducted on Argoverse and nuScenes demonstrate that our method outperforms state-of-the-art methods in some evaluation metrics while achieving the optimal trade-off between predictive accuracy and efficiency. Field experiments conducted in urban environments further validate the effectiveness of the proposed approach.

HoughLaneNet: Lane detection with deep hough transform and dynamic convolution

The task of lane detection has garnered considerable attention in the field of autonomous driving due to its complexity. Lanes can present difficulties for detection, as they can be narrow, fragmented, and often obscured by heavy traffic. However, it has been observed that the lanes have a geometrical structure that resembles a straight line, leading to improved lane detection results when utilizing this characteristic. To address this challenge, we propose a hierarchical Deep Hough Transform (DHT) approach that combines all lane features in an image into the Hough parameter space. Additionally, we refine the point selection method and incorporate a Dynamic Convolution Module to effectively differentiate between lanes in the original image. Our network architecture comprises a backbone network, either a ResNet or Pyramid Vision Transformer, a Feature Pyramid Network as the neck to extract multi-scale features, and a hierarchical DHT-based feature aggregation head to accurately segment each lane. By utilizing the lane features in the Hough parameter space, the network learns dynamic convolution kernel parameters corresponding to each lane, allowing the Dynamic Convolution Module to effectively differentiate between lane features. Subsequently, the lane features are fed into the feature decoder, which predicts the final position of the lane. Our proposed network structure demonstrates improved performance in detecting heavily occluded or worn lane images, as evidenced by our extensive experimental results, which show that our method outperforms or is on par with state-of-the-art techniques.

Lane Detection Network with Direction Context

Lane detection has been a research hot-spot these years for it is a key technique in autonomous driving. As lane detection requires real-time performance on car systems with limited computation ability and memorizer space, most algorithms employ small networks with higher speed but lower accuracy. These methods mostly rely on various distillation techniques or multi-task learning to enhance the overall precision, but few have exploited the direction context which is closely related to lane pixel distribution. In this paper, we tackle the problem of boosting lane detection performance by adding in direction context. For algorithm efficiency, we use the light-weight ERF-Net as our backbone network. Firstly, we incorporate spatial convolutions to propagate lane features directionally. Then we feed four supervision signals by a specially designed Cross-Sum Loss to train these features with rich direction context. We show that the proposed Cross-Sum Loss can enrich the direction sensitivity of lane features, improving both the precision and the interpretability of the lane detection network with negligible time and computation cost during inference. Besides, we also conduct a knowledge distillation step on the direction-sensitive features for a further precision gain. Extensive experiments and analyses conducted on the widely-used CULane dataset illustrate that the proposed method achieves state-of-the-art performance with real-time processing speed, demonstrating the effectiveness of adding in direction context with the Cross-Sum Loss.

Combining Low-Light Scene Enhancement for Fast and Accurate Lane Detection

Lane detection is a crucial task in the field of autonomous driving, as it enables vehicles to safely navigate on the road by interpreting the high-level semantics of traffic signs. Unfortunately, lane detection is a challenging problem due to factors such as low-light conditions, occlusions, and lane line blurring. These factors increase the perplexity and indeterminacy of the lane features, making them hard to distinguish and segment. To tackle these challenges, we propose a method called low-light enhancement fast lane detection (LLFLD) that integrates the automatic low-light scene enhancement network (ALLE) with the lane detection network to improve lane detection performance under low-light conditions. Specifically, we first utilize the ALLE network to enhance the input image’s brightness and contrast while reducing excessive noise and color distortion. Then, we introduce symmetric feature flipping module (SFFM) and channel fusion self-attention mechanism (CFSAT) to the model, which refine the low-level features and utilize more abundant global contextual information, respectively. Moreover, we devise a novel structural loss function that leverages the inherent prior geometric constraints of lanes to optimize the detection results. We evaluate our method on the CULane dataset, a public benchmark for lane detection in various lighting conditions. Our experiments show that our approach surpasses other state of the arts in both daytime and nighttime settings, especially in low-light scenarios.

Multi-lane detection by combining line anchor and feature shift for urban traffic management

Lane detection is a fundamental task in urban traffic management. Like lane detection for automatic driving, lane detection for traffic management will be faced with common challenges including fog, night, shadow, no line and etc. Meanwhile, it will be faced with new and unique challenges including variable number of lanes, blocked lanes due to oversize vehicles and color interference. In this paper, we propose a multi-lane detection method by combining the line anchor and feature shift (MLD-LAFS) to cope with these challenges. The proposed method features a two-branch neural network structure based on the line anchor. The first branch is the feature shift branch incorporating spatial attention, which is designed to enhance the local features of lanes. The second branch is the global information branch incorporating channel attention and cross attention, which is designed to establish the long-distance connection of lane features. The channel attention can obtain the global channel information. The uncoupled feature shift cross attention can obtain the global spatial information. The feature map containing the global information can be obtained by fusing the feature maps of the first and second branches. The line anchor is used as the supervision information to generate the predicted lane and realize multi-lane detection. The feature shift is used to solve the problems of lane line being blocked and color interference. We perform the performance evaluation on three datasets, including CULane, TuSimple and a newly constructed dataset MonitorLane. Experimental results show that the proposed MLD-LAFS achieves remarkable results on the CULane and the TuSimple dataset. Moreover, the proposed MLD-LAFS achieves the highest grading in F1-score on the MonitorLane dataset, compared to existing solutions, including LaneATT, PolyLaneNet, Lane Shape Prediction with Transformers (LSTR) and etc.

LaneTD: Lane Feature Aggregator Based on Transformer and Dilated Convolution

Deep-learning-based algorithms successfully improve the detection accuracy of traffic lanes. Most of them employed stacked convolutional neural networks (CNNs) to extract semantics, but they have trouble gathering global visual information. What is more, the stacked CNNs perform multiple downsampling, which reduces the resolution of feature map, causing the tiny features easy to be ignored. To address these problems, LaneTD is proposed: a novel lane feature aggregator that extracts and fuses local and global features. This method adopts dilated convolution to extract local features of various scales and enhances global representation based on the transformer encoder. The local information and global information are then weighted fused using a bidirectional feature pyramid. In addition, we apply unsupervised image style transfer to improve the performance in nighttime and dark scenarios. The method is quantitatively evaluated on the public challenging CULane dataset, and the result shows that the proposed method significantly improves the accuracy and computational efficiency of lane detection in different scenarios. Furthermore, an ablation study is carried out, and a discussion of efficiency tradeoff choices can be applied in practice.

A General Framework for Reconstructing Full-Sample Continuous Vehicle Trajectories Using Roadside Sensing Data

Vehicle trajectory data play an important role in autonomous driving and intelligent traffic control. With the widespread deployment of roadside sensors, such as cameras and millimeter-wave radar, it is possible to obtain full-sample vehicle trajectories for a whole area. This paper proposes a general framework for reconstructing continuous vehicle trajectories using roadside visual sensing data. The framework includes three modules: single-region vehicle trajectory extraction, multi-camera cross-region vehicle trajectory splicing, and missing trajectory completion. Firstly, the vehicle trajectory is extracted from each video by YOLOv5 and DeepSORT multi-target tracking algorithms. The vehicle trajectories in different videos are then spliced by the vehicle re-identification algorithm fused with lane features. Finally, the bidirectional long-short-time memory model (LSTM) based on graph attention is applied to complete the missing trajectory to obtain the continuous vehicle trajectory. Measured data from Donghai Bridge in Shanghai are applied to verify the feasibility and effectiveness of the framework. The results indicate that the vehicle re-identification algorithm with the lane features outperforms the vehicle re-identification algorithm that only considers the visual feature by 1.5% in mAP (mean average precision). Additionally, the bidirectional LSTM based on graph attention performs better than the model that does not consider the interaction between vehicles. The experiment demonstrates that our framework can effectively reconstruct the continuous vehicle trajectories on the expressway.