Lane Detection Research Articles

Lane detection networks based on deep neural networks and temporal information

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes.

PortLaneNet: A Scene-Aware Model for Robust Lane Detection in Container Terminal Environments

In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities such as intricate ground markings, tire crane lane lines, and various types of regional lines that significantly complicate detection tasks. Our approach includes the novel Scene Prior Perception Module, which leverages pre-training to provide essential prior information for more accurate lane detection. This module capitalizes on the enclosed nature of container terminals, where images from similar area scenes offer effective prior knowledge to enhance detection accuracy. Additionally, our model significantly improves understanding by integrating both high- and low-level image features through attention mechanisms, focusing on the critical components of lane detection. Through rigorous experimentation, PortLaneNet has demonstrated superior performance in port environments, outperforming traditional lane detection methods. The results confirm the effectiveness and superiority of our model in addressing the complex challenges of lane detection in such specific settings. Our work provides a valuable reference for solving lane detection issues in specialized environments and proposes new ideas and directions for future research.

Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving

Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model.

U-Net-Based Learning Using Enhanced Lane Detection with Directional Lane Attention Maps for Various Driving Environments

Recent advancements in optical and electronic sensor technologies, coupled with the proliferation of computing devices (such as GPUs), have enabled real-time autonomous driving systems to become a reality. Hence, research in algorithmic advancements for advanced driver assistance systems (ADASs) is rapidly expanding, with a primary focus on enhancing robust lane detection capabilities to ensure safe navigation. Given the widespread adoption of cameras on the market, lane detection relies heavily on image data. Recently, CNN-based methods have attracted attention due to their effective performance in lane detection tasks. However, with the expansion of the global market, the endeavor to achieve reliable lane detection has encountered challenges presented by diverse environmental conditions and road scenarios. This paper presents an approach that focuses on detecting lanes in road areas traversed by vehicles equipped with cameras. In the proposed method, a U-Net based framework is employed for training, and additional lane-related information is integrated into a four-channel input data format that considers lane characteristics. The fourth channel serves as the edge attention map (E-attention map), helping the modules achieve more specialized learning regarding the lane. Additionally, the proposition of an approach to assign weights to the loss function during training enhances the stability and speed of the learning process, enabling robust lane detection. Through ablation experiments, the optimization of each parameter and the efficiency of the proposed method are demonstrated. Also, the comparative analysis with existing CNN-based lane detection algorithms shows that the proposed training method demonstrates superior performance.

Real-Time Vehicle and Lane Detection using Modified OverFeat CNN: A Comprehensive Study on Robustness and Performance in Autonomous Driving

This examination researches the use of profound learning methods, explicitly utilizing Convolutional Brain Organizations (CNNs), for ongoing recognition of vehicles and path limits in roadway driving situations. The study investigates the performance of a modified Over Feat CNN architecture by making use of a comprehensive dataset that includes annotated frames captured by a variety of sensors, including cameras, LIDAR, radar, and GPS. The framework shows heartiness in identifying vehicles and anticipating path shapes in 3D while accomplishing functional rates of north of 10 Hz on different GPU setups. Vehicle bounding box predictions with high accuracy, resistance to occlusions, and efficient lane boundary identification are key findings. Quiet, the exploration underlines the likely materialness of this framework in the space of independent driving, introducing a promising road for future improvements in this field.

IVehicles: Spatial Feature Aggregation Network for Lane Detection Assistance System

iVehicles: Spatial Feature Aggregation Network for Lane Detection Assistance System

Automated Car: Intelligent Maneuver

Abstract: Automated vehicles are rapidly evolving to meet the demands of modern transportation systems, aiming for enhanced safety, efficiency, and user experience. Autonomous vehicles are also a big part of these technologies. The most important action of a driver has to do is to follow the lanes on the way to the destination. This research presents Intelligent Maneuver, a sophisticated framework integrating four key functionalities crucial for automated car navigation: object avoidance, traffic light detection, self-parking, and lane detection. Leveraging advanced computer vision techniques and sensors fusion our framework enables vehicles to navigate complex urban environments autonomously

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.

Accelerating autonomous vehicles: Harnessing FPGA power for deep learning advancements

In the rapidly developing field of autonomous vehicles (AV), the integration of deep learning algorithms has become the cornerstone for improving vehicle perception and decision-making ability. This study investigates the potential of field-programmable gate arrays (FPGAs) as hardware accelerators for deep-learning tasks in self-driving cars. This study used a Zynq UltraScale+ MPSoC FPGA board from Xilinx, known for its performance and adaptability, and combined it with a high-resolution camera to simulate real-world visual data encountered by AVs. The approach involved implementing a convolutional neural network (CNN) to perform tasks such as object detection, lane detection, and traffic sign classification. The model was quantized and converted to VHDL code using Xilinx Vivado HLS to optimize the deep learning algorithm for FPGA.The results show that the FPGA-based model significantly outperforms the traditional CPU-based model in terms of processing speed and energy efficiency without sacrificing accuracy. This study highlights the critical role of FPGAs in supporting deep learning tasks in self-driving cars and paves the way for safer and more efficient transportation solutions in the future.

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

Lane detection plays a pivotal role in the successful implementation of Advanced Driver Assistance Systems (ADASs), which are essential for detecting the road's lane markings and determining the vehicle's position, thereby influencing subsequent decision making. However, current deep learning-based lane detection methods encounter challenges. Firstly, the on-board hardware limitations necessitate an exceptionally fast prediction speed for the lane detection method. Secondly, improvements are required for effective lane detection in complex scenarios. This paper addresses these issues by enhancing the row-anchor-based lane detection method. The Transformer encoder-decoder structure is leveraged as the row classification enhances the model's capability to extract global features and detect lane lines in intricate environments. The Feature-aligned Pyramid Network (FaPN) structure serves as an auxiliary branch, complemented by a novel structural loss with expectation loss, further refining the method's accuracy. The experimental results demonstrate our method's commendable accuracy and real-time performance, achieving a rapid prediction speed of 129 FPS (the single prediction time of the model on RTX3080 is 15.72 ms) and a 96.16% accuracy on the Tusimple dataset-a 3.32% improvement compared to the baseline method.

Lightweight convolutional neural networks for real-time lane segmentation

Autonomous driving depends on reliable perception systems that involve various perception modules and advanced computer vision techniques. One crucial component of these systems is lane detection, which traditional methods often rely on basic features like color or edges that are sensitive to lighting and perspective changes. Recently, convolutional neural networks (CNNs), have revolutionized lane detection. Nevertheless, existing methods still have some limitations, such as the need for pixel-level labeling and computational inefficiency for real-time applications. To address these challenges, this work leverages PP-LiteSeg for real-time semantic segmentation. PP-LiteSegs key elements are its Simple Pyramid Pooling Module (SPPM), Unified Attention Fusion Module (UAFM), and Flexible and Lightweight Decoder (FLD), which optimize lane detection efficiency. The FLD flexibly adjusts computational costs between the encoder and decoder, balancing efficiency and accuracy. The UAFM enhances feature representations using attention mechanisms, increasing segmentation accuracy. The SPPM efficiently aggregates contextual information while reducing computational complexity. The comprehensive method for lane segmentation achieves competitive results on popular lane detection datasets. The proposed model can adapt to different computational capabilities and significantly enhances lane detection efficiency for real-time applications.

A Hybrid Global-Local Perception Network for Lane Detection

Lane detection is a critical task in autonomous driving, which requires accurately predicting the complex topology of lanes in various scenarios. While previous methods of lane detection have shown success, challenges still exist, especially in scenarios where lane markings are absent. In this paper, we analyze the role of global and local features in accurately detecting lanes and propose a Hybrid Global-Local Perception Network (HGLNet) to leverage them. Global and local features play distinct roles in lane detection by respectively aiding in the detection of lane instances and the localization of corresponding lanes. HGLNet extracts global semantic context by utilizing a global extraction head that aggregates information about adaptive sampling points around lanes, achieving an optimal trade-off between performance and efficiency. Moreover, we introduce a Multi-hierarchy feature aggregator (MFA) to capture feature hierarchies in both regional and local ranges, elevating the representation of local features. The proposed Hybrid architecture can simultaneously focus on global and local features at different depth levels and efficiently integrate them to sense the global presence of lanes and accurately regress their locations. Experimental results demonstrate that our proposed method improves detection accuracy in various challenging scenarios, outperforming the state-of-the-art lane detection methods.

Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection

Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facilitate the practical application of LD models, enabling fast speeds and limited memories without labeled data. However, prior PTQ methods do not consider the complex LD outputs that contain physical semantics, such as offsets, locations, etc., and thus cannot be directly applied to LD models. In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distortion Score. Moreover, we identify two main factors impacting the LD performance after quantization, namely intra-head sensitivity and inter-head sensitivity, where a small quantization error in specific semantics can cause significant lane distortion. Thus, we propose a Selective Focus framework deployed with Semantic Guided Focus and Sensitivity Aware Selection modules, to incorporate post-processing information into PTQ reconstruction. Based on the observed intra-head sensitivity, Semantic Guided Focus is introduced to prioritize foreground-related semantics using a practical proxy. For inter-head sensitivity, we present Sensitivity Aware Selection, efficiently recognizing influential prediction heads and refining the optimization objectives at runtime. Extensive experiments have been done on a wide variety of models including keypoint-, anchor-, curve-, and segmentation-based ones. Our method produces quantized models in minutes on a single GPU and can achieve 6.4\% F1 Score improvement on the CULane dataset. Code and supplementary statement can be found at https://github.com/PannenetsF/SelectiveFocus.

PVALane: Prior-Guided 3D Lane Detection with View-Agnostic Feature Alignment

Monocular 3D lane detection is essential for a reliable autonomous driving system and has recently been rapidly developing. Existing popular methods mainly employ a predefined 3D anchor for lane detection based on front-viewed (FV) space, aiming to mitigate the effects of view transformations. However, the perspective geometric distortion between FV and 3D space in this FV-based approach introduces extremely dense anchor designs, which ultimately leads to confusing lane representations. In this paper, we introduce a novel prior-guided perspective on lane detection and propose an end-to-end framework named PVALane, which utilizes 2D prior knowledge to achieve precise and efficient 3D lane detection. Since 2D lane predictions can provide strong priors for lane existence, PVALane exploits FV features to generate sparse prior anchors with potential lanes in 2D space. These dynamic prior anchors help PVALane to achieve distinct lane representations and effectively improve the precision of PVALane due to the reduced lane search space. Additionally, by leveraging these prior anchors and representing lanes in both FV and bird-eye-viewed (BEV) spaces, we effectively align and merge semantic and geometric information from FV and BEV features. Extensive experiments conducted on the OpenLane and ONCE-3DLanes datasets demonstrate the superior performance of our method compared to existing state-of-the-art approaches and exhibit excellent robustness.

GSENet:Global Semantic Enhancement Network for Lane Detection

Lane detection is the cornerstone of autonomous driving. Although existing methods have achieved promising results, there are still limitations in addressing challenging scenarios such as abnormal weather, occlusion, and curves. These scenarios with low visibility usually require to rely on the broad information of the entire scene provided by global semantics and local texture information to predict the precise position and shape of the lane lines. In this paper, we propose a Global Semantic Enhancement Network for lane detection, which involves a complete set of systems for feature extraction and global features transmission. Traditional methods for global feature extraction usually require deep convolution layer stacks. However, this approach of obtaining global features solely through a larger receptive field not only fails to capture precise global features but also leads to an overly deep model, which results in slow inference speed. To address these challenges, we propose a novel operation called the Global feature Extraction Module (GEM). Additionally, we introduce the Top Layer Auxiliary Module (TLAM) as a channel for feature distillation, which facilitates a bottom-up transmission of global features. Furthermore, we introduce two novel loss functions: the Angle Loss, which account for the angle between predicted and ground truth lanes, and the Generalized Line IoU Loss function that considers the scenarios where significant deviations occur between the prediction of lanes and ground truth in some harsh conditions. The experimental results reveal that the proposed method exhibits remarkable superiority over the current state-of-the-art techniques for lane detection.Our codes are available at:https://github.com/crystal250/GSENet.

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.

Autonomous Lane Detection Car using Raspberry PI and Open CV

This research paper introduces an innovative method for real-time autonomous lane detection utilizing a Raspberry Pi embedded system and the OpenCV computer vision library. The primary objective is to improve vehicle navigation capabilities by precisely identifying lane boundaries from input video streams. The system's robust performance across diverse environmental conditions is achieved through the computational power of the Raspberry Pi platform and the flexibility of OpenCV. The methodology encompasses image preprocessing, lane detection algorithms, and seamless integration with the Raspberry Pi hardware. Through comprehensive experimental results, the proposed approach showcases its effectiveness and efficiency in autonomously detecting lanes. This research contributes to the development of safer and more reliable autonomous driving systems

Lanesense: A Computer Vision Marvel for Accurate Lane Detection

Lanesense: A Computer Vision Marvel for Accurate Lane Detection

Enhancing lane detection with a lightweight collaborative late fusion model

Research in autonomous systems is gaining popularity both in academia and industry. These systems offer comfort, new business opportunities such as self-driving taxis, more efficient resource utilization through car-sharing, and most importantly, enhanced road safety. Different forms of Vehicle-to-Everything (V2X) communication have been under development for many years to enhance safety. Advances in wireless technologies have enabled more data transmission with lower latency, creating more possibilities for safer driving. Collaborative perception is a critical technique to address occlusion and sensor failure issues in autonomous driving. To enhance safety and efficiency, recent works have focused on sharing extracted features instead of raw data or final outputs, leading to reduced message sizes compared to raw sensor data. Reducing message size is important to enable collaborative perception to coexist with other V2X applications on bandwidth-limited communication devices.To address this issue and significantly reduce the size of messages sent while maintaining high accuracy, we propose our model: LaCPF (Late Collaborative Perception Fusion), which uses deep learning for late fusion. We demonstrate that we can achieve better results while using only half the message size over other methods. Our late fusion framework is also independent of the local perception model, which is essential, as not all vehicles on the road will employ the same methods. Therefore LaCPF can be scaled more quickly as it is model and sensor-agnostic.

Research on multi-task perception network of traffic scene based on feature fusion1

Aiming at the problems of low precision and low real-time performance when deploying to embedded platforms in existing multi-task networks, this paper proposes a traffic scene multi-task perception network model (ETS_YOLOP) based on feature fusion. Firstly, an Efficient Attention Control Aggregation Network Module (EACAN) is constructed to improve the real-time perception of the model, and the Space Pyramid Pool Fast Convolutional Module (SPPFCSPC) is used at the end of the backbone network to increase the receptive field. Finally, a Multiscale Convolution Transformer Fusion Module (CTFM) is designed in the task branch to better capture global information and rich context information. The experimental results show that compared with the YOLOP model, the ETS_YOLOP model has a significant improvement in perception accuracy, 156% in real-time performance, 0.4% increase in mAP on the object detection task, 0.5% increase in mIoU on the drivable area segmentation task, and an 11.4% increase in accuracy on the lane detection task. In order to verify the real-time perception of the model on the embedded platform, the ETS_YOLOP model is deployed on the Huawei MDC300F computing platform. Under the condition of the image input size of 640×640, the average frame rate can reach 55FPS, which can realize real-time perception on the embedded platform.