Lane Detection Algorithm Research Articles

Lane Line and Object Detection Using Yolo v3

In the contemporary age, creating autonomous vehi- cles is a crucial starting point for the advancement of intelligent transportation systems that rely on sophisticated telecommu- nications network infrastructure, including the upcoming 6g networks. The paper discusses two significant issues, namely, lane detection and obstacle detection (such as road signs, traffic lights, vehicles ahead, etc.) using image processing algorithms. To address issues like low accuracy in traditional image processing methods and slow real-time performance of deep learning-based methods, barriers for smart traffic lane and object detection algorithms are proposed. We initially rectify the distorted image resulting from the camera and then employ a threshold algorithm for the lane detection algorithm. The image is obtained by extracting a specific region of interest and applying an inverse perspective transform to obtain a top-down view. Finally, we apply the sliding window technique to identify pixels that belong to each lane and modify it to fit a quadratic equation. The Yolo algorithm is well-suited for identifying various types of obstacles, making it a valuable tool for solving identification problems. Finally, we utilize real-time videos and a straightforward dataset to conduct simulations for the proposed algorithm. The simula- tion outcomes indicate that the accuracy of the proposed method for lane detection is 97.91% and the processing time is 0.0021 seconds. The proposal for detecting obstacles has an accuracy rate of 81.90% and takes only 0.022 seconds to process. Compared to the conventional image processing technique, the proposed method achieves an average accuracy of 89.90% and execution time of 0.024 seconds, demonstrating a robust capability against noise. The findings demonstrate that the suggested algorithm can be implemented in self-driving car systems, allowing for efficient and fast processing of the advanced network.

A Robust Target Detection Algorithm Based on the Fusion of Frequency-Modulated Continuous Wave Radar and a Monocular Camera

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust.

Pedestrian detection using a MEMS acoustic array mounted on a moving vehicle

Automatic pedestrian detection in a vehicle is of vital importance in Advanced Driver Assistance Systems (ADAS). Sensors currently used are based on cameras, radars and lidars, whose performance is degraded in environments with reduced visibility: night, smoke, fog, etc. This paper has validated the use of an active array of 150 MEMS (Micro-Electro Mechanical Systems) microphones incorporated into a conventional car moving in real urban traffic conditions, with the system working in real time, at a rate of 8 detections per second. Together with beamforming, Constant False Alarm Rate (CFAR) detection and lane detection algorithms, a crucial algorithm has been incorporated for the proper performance of the system that discriminates the detections of objects or pedestrians generated by the system from the false alarms generated by road imperfections, such as bumps, cracks, etc. Based on 6000 captures, performed with the car moving at 30 km/h, which is the typical speed limit in urban environments, it has been possible to detect pedestrians positioned at a distance from the car varying between 5 and 20 m, with a detection probability of 0.91 and a false alarm probability of 0.01. The results obtained have validated the effectiveness of using active acoustic arrays in the field of pedestrian detection and position estimation from moving cars in urban environments. The fusion of the presented system with the systems currently used for this purpose, would significantly improve the performance of pedestrian detection systems interacting with AEB (Automatic Emergency Breaking) systems, extending their operability to environments with reduced visibility, and resulting in the reduction in the number of possible collisions with pedestrians, thus increasing their safety.

Towards Precision Navigation: Curved lane Detection System in Vehicles

Abstract: Curved lane detection is a critical componentof advanced driver assistance systems (ADAS) and autonomous vehicles, playing a pivotal role in ensuring safeand efficient navigation on complex roadways. In the pursuitof enhancing accuracy and expediting processing speed in road lane detection, researchers have proposed numerous methodologies. Despite these efforts, several challenges persist, including variations in lane markings, fluctuations in lighting conditions, and the emergence of shadows. Addressing these obstacles is crucial for establishing dependable lane detection systems. The curve lane detection algorithm exhibits a robust performance, as evidenced by the experimental results, which consistently reveal a notably high success rate in accurately identifying and delineating curved lanes

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

A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

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.

Detect Lane Line for Self-Driving Car Using Hue Saturation Lightness and Hue Saturation Value Color Transformation

Self-driving vehicles require the ability to perceive and understand their surroundings, just like human drivers. It entails navigating efficiently on roads, obeying traffic signs and signals, and avoiding collisions with other vehicles and pedestrians. To address the challenges associated with object detection in self-driving cars, an effort was made to demonstrate lane detection using the OpenCV library. To achieve this goal, the well-established probabilistic Hough transform technique is used for line detection. Before applying Hough transforms, several pre-processing techniques are used, including converting the image to grayscale, camera calibration, and implementing a masking filter. In addition, edge detection is performed using the edge detection method. The study also indicates a preference for the use of HSL (Hue, Saturation, and Lightness) and HSV (Hue, Saturation, Value) color spaces. When HSL is applied, white lines appear purer and brighter, resulting in superior performance compared to using HSV specifically to detect white. This algorithm proved particularly effective in detecting straight lanes, which achieved an accuracy ratio of 96.06%. By incorporating these methodologies, the lane detection algorithm implemented with the OpenCV library addresses the challenges of self-driving vehicles, providing them with improved perception capabilities similar to human drivers.

A Fast and Robust Lane Detection via Online Re-Parameterization and Hybrid Attention.

Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks.

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.

Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion

Identifying lane markings is a key technology in assisted driving and autonomous driving. The traditional sliding window lane detection algorithm has good detection performance in straight lanes and curves with small curvature, but its detection and tracking performance is poor in curves with larger curvature. Large curvature curves are common scenes in traffic roads. Therefore, in response to the problem of poor lane detection performance of traditional sliding window lane detection algorithms in large curvature curves, this article improves the traditional sliding window algorithm and proposes a sliding window lane detection calculation method, which integrates steering wheel angle sensors and binocular cameras. When a vehicle first enters a bend, the curvature of the bend is not significant. Traditional sliding window algorithms can effectively detect the lane line of the bend and provide angle input to the steering wheel, enabling the vehicle to travel along the lane line. However, as the curvature of the curve increases, traditional sliding window lane detection algorithms cannot track lane lines well. Considering that the steering wheel angle of the car does not change much during the adjacent sampling time of the video, the steering wheel angle of the previous frame can be used as input for the lane detection algorithm of the next frame. By using the steering wheel angle information, the search center of each sliding window can be predicted. If the number of white pixels within the rectangular range centered around the search center is greater than the threshold, the average of the horizontal coordinate values of these white pixels will be used as the horizontal coordinate value of the sliding window center. Otherwise, the search center will be used as the center of the sliding window. A binocular camera is used to assist in locating the position of the first sliding window. The simulation and experimental results show that compared with traditional sliding window lane detection algorithms, the improved algorithm can better recognize and track lane lines with large curvature in bends.

Lightweight real-time lane detection algorithm based on ghost convolution and self batch normalization

Lightweight real-time lane detection algorithm based on ghost convolution and self batch normalization

Semi-supervised lane detection for continuous traffic scenes

Objective This article aims to upgrade the lane detection algorithm from image to video level in order to advance automatic driving technology. The objective is to propose a cost-efficient algorithm that can handle complex traffic scenes and different driving speeds using continuous image inputs. Methods To achieve this objective, we introduce the Multi-ERFNet-ConvLSTM network framework, which combines Efficient Residual Factorized ConvNet (ERFNet) and Convolution Long Short Term Memory (ConvLSTM). Additionally, we incorporate the Pyramidally Attended Feature Extraction (PAFE) Module into our network design to effectively handle multi-scale lane objects. The algorithm is evaluated using a divided dataset and comprehensive assessments are conducted across multiple dimensions. Results In the testing phase, the Multi-ERFNet-ConvLSTM algorithm surpasses the primary baselines and demonstrates superior performance in terms of Accuracy, Precision, and F1-score metrics. It exhibits excellent detection results in various complex traffic scenes and performs well at different driving speeds. Conclusions The proposed Multi-ERFNet-ConvLSTM algorithm provides a robust solution for video-level lane detection in advanced automatic driving. By utilizing continuous image inputs and incorporating the PAFE Module, the algorithm achieves high performance while reducing labeling costs. Its exceptional accuracy, precision, and F1-score metrics highlight its effectiveness in complex traffic scenarios. Moreover, its adaptability to different driving speeds makes it suitable for real-world applications in autonomous driving systems.

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.

Empirical investigation of lane usage, lane changing and lane choice phenomena in a multimodal urban arterial

The combination of multimodal interactions, dynamic congestion and lane changing phenomena in an urban environment can have a strong impact on traffic flow dynamics, active road capacity and road safety. However, while there is a vast understanding of microscopic traffic models for motorways with respect to lane changes and lane choices, there are not many empirical observations of the phenomena at the urban scale. Recently, new data collection techniques with unmanned aerial systems have emerged offering unique opportunities to study complex traffic phenomena. This paper investigates the interrelation between lane changing and lane choice using the pNEUMA dataset in one of the busiest arterials in Athens, Greece. The main concept relies on the definition of two layers regarding how the multiple lanes of congested arterials are actually being used. Specifically, we show that the marked lanes on the arterial (marked layer) are influenced by the frictions created by static and moving bottlenecks (bus and taxi stops, illegal parking, etc.). Using an existing lane detection algorithm, we show that there is an active layer which is different to the marked lanes on the road affecting capacity in the macroscopic level and driving behavior in the microscopic level. The analysis on lane changing highlights the conflicting areas where more lane changes can occur. Furthermore, the particularity of Powered-Two-Wheelers’ lane changing behavior is examined and evidence on the PTW filtering phenomenon is provided. Following, the analysis on driver lane choice shows how turning vehicles approach the target lane and the differences between left- and right-turning vehicles.

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.

Interpolation-Based Framework for Generation of Ground Truth Data for Testing Lane Detection Algorithm for Automated Vehicle

Automated vehicles, predicted to be fully electric in future, are expected to reduce road fatalities and road traffic emissions. The lane departure warning system, an important feature of automated vehicles, utilize lane detection and tracking algorithms. Researchers are constrained to test their lane detection algorithms because of the small publicly available datasets. Additionally, those datasets may not represent differences in road geometries, lane marking and other details unique to a particular geographic location. Existing methods to develop the ground truth datasets are time intensive. To address this gap, this study proposed a framework for an interpolation approach for quickly generating reliable ground truth data. The proposed method leverages the advantage of the existing manual and time-slice approaches. A detailed framework for the interpolation approach is presented and the performance of the approach is compared with the existing methods. Video datasets for performance evaluation were collected in Melbourne, Australia. The results show that the proposed approach outperformed four existing approaches with a reduction in time for generating ground truth data in the range from 4.8% to 87.4%. A reliable and quick method for generating ground truth data, as proposed in this study, will be valuable to researchers as they can use it to test and evaluate their lane detection and tracking algorithms.

Comparison of GPU and FPGA Hardware Acceleration of Lane Detection Algorithm

Comparison of GPU and FPGA Hardware Acceleration of Lane Detection Algorithm

Artificial Intelligence based Lane Detection using Satellite Images

Lane detection is one of the most important task for tragedy or emergency response using satellite imagery structures. Most of the researchers adopted Machine Learning (ML) approaches to detect white lines on the roads namely lanes. These approaches are more complex and computational. In order to identify knowledge gaps, this study compares and analyses the various lane detection and tracking algorithms that have been used in research papers in the past. Additionally, it provides an overview of some of the datasets used in various research papers. The main challenge in detecting the lane using satellite images is the weather, which makes it difficult to achieve the expected objectives.

Three‑Dimensional Lane Detection Algorithm of Lidar Based on Adaptive Gating and Dual Pathways

Three‑Dimensional Lane Detection Algorithm of Lidar Based on Adaptive Gating and Dual Pathways