Lane Boundaries Research Articles

Road Sense: Intelligent Road Monitoring System

Abstract: This project proposes a system for detecting traffic signals, lane layouts, and speed bumps in road infrastructure using video footage through machine learning. Lane detection is performed through region of interest selection and edge detection. Lane lines are extracted based on specific characteristics. A deep learning model is trained to detect lane boundaries and road curvature. The system provides real-time alerts and recommendations to enhance road safety and driving experiences.

ROAD LANE LINE DETECTION USING DEEP LEARNING

This model introduces a many technical advancements have recently been made in the field of road safety, as accidents are being increasing day by day, and one of the main causes of such accidents is a driver's lack of attention. To reduce the incidence of accidents and keep safe, technological improvements should be made. One model is to use Lane Detection Systems, which function by identifying lane borders on the road and alerting the driver if he switches to an wrong lane marking. A lane detection system is an crucial part of many technologically advanced transportation systems. Anyhow it is a difficult goal to fulfil due to the various road conditions that a person finds, particularly when driving at night or in daytime. A camera positioned on the front of the car catches the view of the road and detects lane boundaries. The model used in this research divides the video image into a series of sub-images and generates image-features for each of them, which are then used to recognize the lanes on the road. Several methods for detecting lane markings on the road have been presented. Keywords: open cv , CNN, Deep Learning.

Robust autonomous vehicle control by leveraging multi-stage MPC and quantized CNN in HIL Framework

This study presents a novel path planning and path-following system designed explicitly for real-world driving conditions in autonomous vehicles. The pipeline considers uncertainties in the dynamic parameters of the vehicle, which are common in real-world scenarios. To address these uncertainties, a robust multi-stage model predictive control approach is developed to generate optimal control outputs. A customized convolutional neural network, Nano-FastSCNN, quantized to half-precision, is employed for accurately segmenting left and right lane boundaries. To ensure that our visual planner is robust against adverse weather conditions, we have rigorously evaluated its generalization capabilities under challenging weather types. The centerline of these lanes is then selected as the desired path and transformed into the camera reference frame by leveraging the camera projection matrix. The controller utilizes this reference trajectory to guide the vehicle along the desired trajectory. To demonstrate real-time processing capabilities, the entire pipeline, including the vision system module and controller, is implemented in a hardware-in-the-loop setup utilizing three NVIDIA Jetson devices to demonstrate the scalability of our architecture. Comprehensive evaluations demonstrate that the pipeline achieves an inference speed of 15.6 FPS, even on the less powerful NVIDIA Jetson Nano. Moreover, the system successfully alleviates the detrimental effects of uncertainties and reliably follows the desired path.

Advanced Road Lane Detection System

Advanced Road Lane Detection System

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.

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

Review on Computer Vision Techniques to enhance Road Lane Detection by using Open CV

In response to the alarming increase in road accidents attributed to driver inattention, recent advancements in road safety technology have become imperative. Addressing this concern, Lane Detection Systems have emerged as a pivotal component in technologically advanced transportation systems. These systems function by identifying lane boundaries on the road and promptly alerting the driver if there is deviation from correct lane markings. However, achieving robust lane detection poses challenges due to diverse road conditions encountered during driving, including night time and day time scenarios. This research focuses on a Lane Detection System that employs a front-facing camera to capture the road view and identify lane boundaries. The proposed method involves dividing the video image. This research presents the study explores various techniques for detecting lane markings, series of sub-images, extracting image features for each segment, and utilizing these features to recognize lanes on the road, acknowledging the complexity introduced by different driving conditions.

Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows

Improving the capacity of intersections is the key to enhancing road traffic systems. Benefiting from the application of Connected Automated Vehicles (CAVs) in the foreseeing future, it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs. Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries. However, it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles (HVs). To address the research gap, this paper proposes a spatiotemporal-restricted A∗ algorithm to obtain efficient and flexible lane-free trajectories for CAVs. First, we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors. Second, we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm. Third, we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs. The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A∗ algorithm, while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency. The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.

Modular Architecture of Advanced Driver Assistance Systems for Effective Traffic Sign Recognition

Analysis of modern approaches to the implementation of driver assistance systems, as well as the implementation of the architecture of the driver assistance system, aimed at recognizing traffic signs at the maximum distance from it under difficult weather conditions, for early feedback to the driver. The paper considers the main signals used in the implementation and operation of the driver assistance system: data from the car's CAN bus, information from a GPS receiver, video fragments from a digital camera. The presented modular architecture uses the listed data sources for estimating the traffic situation, as well as neural network methods for recognizing traffic signs. The modular architecture of the driver assistance system is presented, which allows notifying the driver about traffic signs. The system is equipped with lane boundary control to alert the driver to signs related to the adjacent carriageway when turning. It has been experimentally proven that the modular architecture of the driver assistance system presented in the paper is not inferior in speed and accuracy to alternative systems, acting as a comprehensive autonomous solution.

A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field.

Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle's forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle's kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle's motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments.

Influence of continuous edge-line delineation on drivers’ lateral positioning in curves: a gaze-steering approach

Recent research indicates that installing shoulders on rural roads for safety purposes causes drivers to steer further inside on right bends and thus exceed lane boundaries. The present simulator study examined whether continuous rather than broken edge-line delineation would help drivers to keep their vehicles within the lane. The results indicated that continuous delineation significantly impacts the drivers’ gaze and steering trajectories. Drivers looked more towards the lane centre and shifted their steering trajectories accordingly. This was accompanied by a significant decrease in lane-departure frequency when driving on a 3.50-m lane but not on a 2.75-m lane. Overall, the findings provide evidence that continuous delineation influences steering control by altering the visual processes underlying trajectory planning. It is concluded that continuous edge-line delineation between lanes and shoulders may induce safer driver behaviour on right bends, which has potential implications for preventing run-off-road crashes and cyclist safety. Practitioner summary: This study examined how continuous and broken edge lines influence driving behaviour around bends with shoulders. With continuous delineation, drivers gazed and steered in the bend further from the edge line and thus had fewer lane departures. Continuous marking can therefore help prevent run-off-road crashes and improve cyclists’ safety.

Improving the Accuracy of Lane Detection by Enhancing the Long-Range Dependence

Lane detection is a common task in computer vision that involves identifying the boundaries of lanes on a road from an image or a video. Improving the accuracy of lane detection is of great help to advanced driver assistance systems and autonomous driving that help cars to identify and keep in the correct lane. Current high-accuracy models of lane detection are mainly based on artificial neural networks. Among them, CLRNet is the latest famous model, which attains high lane detection accuracy. However, in some scenarios, CLRNet attains lower lane detection accuracy, and we revealed that this is caused by insufficient global dependence information. In this study, we enhanced CLRNet and proposed a new model called NonLocal CLRNet (NLNet). NonLocal is an algorithmic mechanism that captures long-range dependence. NLNet employs NonLocal to acquire more long-range dependence information or global information and then applies the acquired information to a Feature Pyramid Network (FPN) in CLRNet for improving lane detection accuracy. Using the CULane dataset, we trained NLNet. The experimental results showed that NLNet outperformed state-of-the-art models in terms of accuracy in most scenarios, particularly in the no-line scenario and night scenario. This study is very helpful for developing more accurate lane detection models.

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

The adaptive cruise control (ACC) system as a typical advanced driver assistant system (ADAS) has been commercially application in automotive industry for decades. An innovative method is proposed in this paper for scene recognition and target tracking for ACC application in some complex traffic environment. Firstly, a multi-sensor fusion method is established to estimate the curvature integrated by the quadratic programing (QP)-based lane boundaries detection, vehicle dynamics of lateral motion, and an improved Kalman filter (IKF) to introduce more measurement information into the feedback correction process. Then, the closet in-path vehicle (CIPV) can be selected according to the statistical distance between the tracked targets and the predicted driving path of ego vehicle. To distinguish the lane changing and curve driving behaviors, the trajectory models of obstacles are established as an ellipsoid domain equation and transformed into a regression model, which is recast as a standardized QP problem. Hence, the behaviors and scenes can be recognized effectively. To restrain the disturbance and improve the accuracy and robustness of target tracking, an [Formula: see text]-based switched tracking method is proposed by combining of the low pass filter (LPF) and [Formula: see text] theory. Finally, an accurate and robust tracker is provided for the CIPV by incorporating with four steps: IKF-based curvature fusion estimation, CIPV selection, QP-based scene recognition, and the [Formula: see text]-based observer. Moreover, two real car experiments are adopted and the results verify the effectiveness and real-time performance of the proposed method.

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

<span lang="EN-US">Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.</span>

Research into Autonomous Vehicles Following and Obstacle Avoidance Based on Deep Reinforcement Learning Method under Map Constraints

Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained by some map elements such as lane boundaries, lane driving rules, and lane center lines. In this paper, a deep reinforcement learning approach seriously considering map elements is proposed to deal with the autonomous driving issues of vehicles following and obstacle avoidance. When the deep reinforcement learning method is modeled, an obstacle representation method is proposed to represent the external obstacle information required by the ego vehicle input, aiming to address the problem that the number and state of external obstacles are not fixed.

A Brief Study on Lane Detection using Lane Boundary Marker Network

Both advanced driver assistance systems and self-driving automobiles rely heavily on lane detection. While current techniques utilize a scope of highlights from low-level to profound highlights separated from convolutional brain networks, they all experience the ill effects of the issue of impediment and battle to distinguish paths with low or no proof on the street. Key points along the lane boundaries are identified using a lane boundary marker network in this paper. An backwards viewpoint planning is assessed utilizing street geometry which is then applied to the recognized markers and lines/bends are fitted together on the amended focuses. Finally, missing path limits are anticipated utilizing path geometry limitations i.e., equidistant and parallelism. Reciprocal weighted averaging guarantees path limits with solid proof rule their anticipated other options. This demonstrates our algorithm's resistance to occluded and missing lanes. We also demonstrate that our algorithm can be used in conjunction with other lane detectors to enhance their lane retrieval capabilities.

Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racing

Autonomous car racing is a challenging task, as it requires precise applications of control while the vehicle is operating at cornering speeds. Traditional autonomous pipelines require accurate pre-mapping, localization, and planning, which make the task computationally expensive and environment-dependent. Recent works propose use of imitation and reinforcement learning to train end-to-end deep neural networks and have shown promising results for highspeed racing. However, the end-to-end models may be dangerous to be deployed on real systems, as the neural networks are treated as black-box models devoid of any provable safety guarantees. In this work we propose a decoupled approach where an optimal end-to-end controller and a state prediction end-to-end model are learned together, and the predicted state of the vehicle is used to formulate a control barrier function for safeguarding the vehicle to stay within lane boundaries. We validate our algorithm both on a high-fidelity Carla driving simulator and a 1/10-scale RC car on a real track. The evaluation results suggest that using an explicit safety controller helps to learn the task safely with fewer iterations and makes it possible to safely navigate the vehicle on the track along the more challenging racing line.

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.

An Autonomous Framework for Real-Time Wrong-Way Driving Vehicle Detection from Closed-Circuit Televisions

Around 1.3 million people worldwide die each year because of road traffic crashes. There are many reasons which cause accidents, and driving in the wrong direction is one of them. In our research, we developed an autonomous framework called WrongWay-LVDC that detects wrong-way driving vehicles from closed-circuit television (CCTV) videos. The proposed WrongWay-LVDC provides several helpful features such as lane detection, correct direction validation, detecting wrong-way driving vehicles, and image capturing features. In this work, we proposed three main contributions: first, the improved algorithm for road lane boundary detection on CCTV (called improved RLB-CCTV) using the image processing technique. Second is the Distance-Based Direction Detection (DBDD) algorithm that uses the deep learning method, where the system validates and detects wrong-driving vehicles. Lastly, the Inside Boundary Image (IBI) capturing feature algorithm captures the most precise shot of the wrong-way-of-driving vehicles. As a result, the framework can run continuously and output the reports for vehicles’ driving behaviors in each area. The accuracy of our framework is 95.23%, as we tested with several CCTV videos. Moreover, the framework can be implemented on edge devices with real-time speed for functional implementation and detection in various areas.

Lane Detection on Roads using Computer Vision

Lane Detection Systems which functions with the purpose to fete the lane borders on road and farther prompts the motorist if he changes and moves to wrong lane markings. Lane detecting system is an essential element of numerous technologically intelligent transport systems. Although it’s a complicate thing to reach because of scrupling road conditions that a person encounters especially while driving at night or indeed in daylight. Lane boundaries are detected using a camera that captures the view of the road, mounted on the front of the vehicle. Given a picture captured from a camera hooked up to a vehicle moving on a road during which captured road could or might not be leveled, or have easily described edges, or some former conceded patterns thereon, also road discovery from an image will be applied to hunt out the road in a picture so it might used as a place in robotization of driving system within the vehicles for moving the vehicle in precise road. In this system of chancing the road within the image prisoner by the vehicle, we're suitable to use some algorithms for vanish point discovery, exploitation Hough Transformation Space, chancing the region of interest, edge discovery exploitation canny edge discovery for road discovery. We've a tendency to use thousands of filmland of colorful roads to trainer our model so the model might notice the road as a result within the new image reused through the vehicle.