Lane Departure Warning System Research Articles

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles.

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS's recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.

Using naturalistic and driving simulator data to model driver responses to unintentional lane departures

Unintentional lane departures on straight roads cause many road fatalities each year. The objective of this study was to explore and model drivers’ recovery steering maneuvers in unintentional drift situations, to enable the prospective safety benefit assessment of lane departure warning and avoidance systems through counterfactual simulations. The timing and amplitude of the steering adjustments drivers make to avoid lane departure were studied over three data sets with different origins, consisting of both naturalistic data and experimental data from a driving simulator study. With respect to timing, the main finding was that visually distracted drivers often initiate the corrective steering response prior to looking back towards the road, demonstrating that lane-keeping information in the visual periphery is sufficient to trigger the response. As for steering amplitude, the observed amplitudes were correlated against different lane departure risk metrics from the literature, resulting in a model capable of accounting for human behavior across all three data sets with good performance. Surprisingly, a very simple model (which describes the steering amplitude as increasing quadratically with the vehicle’s orientation to the road) predicted the amplitude of the primary corrective steering adjustment better than models based on more complex lane departure risk metrics. This result indicates that drivers scale the amplitude of their steering adjustment to the steering input needed to get the vehicle back in the lane already at first response. However, it was possible to obtain a similar model fit using a more complex threshold model, with different dynamics depending on the vehicle’s current orientation to the road. We discuss how these findings can be applied to models of human steering for safety benefit assessment.

A neuroergonomics approach to driver’s cooperation with Lane Departure Warning Systems

Lane Departure Warning Systems (LDWS) are automation that warn drivers in case of immediate lane departure. While LDWS are associated with increased road safety, little is known about the neural aspects of the cooperation between an LDWS and the driver behind the wheel. The present study addresses this issue by combining fMRI and driving simulation for experienced and novice drivers. The results reveal brain areas activated immediately after warning: it involves areas linked to the alertness network (midbrain, thalamus, anterior cingulate cortex), to motor actions and planning (motor and premotor cortexes; BA4/6 -cerebellum) and to attentional redirection (superior frontal cortex; BA10). There were no differences between experienced and novice drivers in this network of cerebral areas. However, prior driving experience mediates the number of lane departures. The results allow for refining a model of cooperation proposed earlier in the literature, by adding a cerebral dimension.

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

PurposeThis article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.Design/methodology/approachRobots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.FindingsOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.Originality/valueOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

LEHA: A novel lightweight efficient and highly accurate lane departure warning system

LEHA: A novel lightweight efficient and highly accurate lane departure warning system

Visual and steering behaviours during lane departures: a longitudinal study of interactions between lane departure warning system, driving task and driving experience

Lane Departure Warning Systems (LDWS) generate a warning in case of imminent lane departure. LDWS have proven to be effective and associated human-machine cooperation modelled. In this study, LDWS acceptance and its impact on visual and steering behaviour have been investigated over 6 weeks for novice and experienced drivers. Unprovoked lane departures were analysed along three driving tasks gradually more demanding. These observations were compared to a baseline condition without automation. The number of lane departures and their duration were dramatically reduced by LDWS, and a narrower visual spread of search during lane departure events was recorded. The findings confirmed LDWS effectiveness and suggested that these benefits are supported by visuo-attentional guidance. No specific influence of driving experience on LDWS was found, suggesting that similar cognitive processes are engaged with or without driving experience. Drivers’ acceptance of LDWS lowered after automation use, but LDWS effectiveness remained stable during prolonged use. Practitioner summary: Lane Departure Warning Systems (LDWS) have been designed to prevent lane departure crashes. Here, LDWS assessment over a 6-week period showed a major drop in the number of lane departure events increasing over time. LDWS effectiveness is supported by the guidance of drivers’ visual attention during lane departure events.

Vision-Based Ingenious Lane Departure Warning System for Autonomous Vehicles

Lane detection is necessary for developing intelligent Autonomous Vehicles (AVs). Using vision-based lane detection is more cost-effective, requiring less operational power. Images captured by the moving vehicle include varying brightness, blur, and occlusion caused due to diverse locations. We propose a Vision-based Ingenious Lane Departure Warning System (VILDS) for AV to address these challenges. The Generative Adversarial Networks (GAN) of the VILDS choose the most precise features to create images that are identical to the original but have better clarity. The system also uses Long Short-Term Memory (LSTM) to learn the average behavior of the samples to forecast lanes based on a live feed of processed images, which predicts incomplete lanes and increases the reliability of the AV’s trajectory. Further, we devise a strategy to improve the Lane Departure Warning System (LDWS) by determining the angle and direction of deviation to predict the AV’s Lane crossover. An extensive evaluation of the proposed VILDS system demonstrated the effective working of the lane detection and departure warning system modules with an accuracy of 98.2% and 96.5%, respectively.

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.

Assessment of the functional safety of the lane departure warning system

This paper describes the advantages of using functional safety analysis according to the ISO 26262 standard. And also the necessary analyses (HAZOP, HAZOP, FMEA, FTA) for the LDWS system were performed and the rating has been determined automotive safety integrity level ASIL. As a result of the analysis of the developed lane departure warning system, 36 technical safety requirements were formulated. Assigned 8 security mechanisms, which are implemented by the software. Tests of the lane departure warning system for the effectiveness of the proposed requirements showed more stable operation of the system integrated into the transport vehicle. Keywords: functional safety, ASIL, ISO 26262, LDWS

Lane Detection in Autonomous Vehicles: A Systematic Review

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

A learning-based evaluation for lane departure warning system considering driving characteristics

Misunderstanding the driver behavior in the next short time is the primary reason of the false warning for the lane departure warning system. This paper proposes a learning-based evaluation to predict whether the driver notices the deviation of the vehicle and takes corrective actions. First, statistical Gaussian model and K-means clustering method are utilized to classify driving style of drivers and determine warning areas based on key driving parameters extracted in driving scenarios. Then, according to the vehicle trajectory in the warning area and the time of lane crossing (TLC) value of the two warning area boundaries, an advanced horizontal dual-area warning (HDAW) model that is trained by bi-direction long short-term memory (BiLSTM) originated from recurrent neural network (RNN) is applied to predict the lane departure and corrective behavior of driver. The personalized warning strategy is finally developed by considering driver characteristics, which allows the warning system to adapt to different driving styles of drivers. Natural driving data from 57 drivers in the experimental driving simulator are collected to train personalized prediction and verify proposed evaluation method. The recent directional sequence of piecewise lateral slopes (DSPLS) and traditional TLC are also researched and compared. Experimental results show that the proposed approach has as low as false alarm rate of 3.97% and can improve prediction accuracy approximately 41.39% over DSPLS method.

Drowsiness Detection System Based on Machine Learning Using Eye State

Drowsiness is one of the major causes of driver-induced traffic accidents. The interactive systems developed to reduce road accidents by alerting drivers is called as Advanced Driver Assistance Systems (ADAS). The most important ADAS are Lane Departure Warning System, Front Collision Warning System and Driver Drowsiness Systems. In this study, an ADAS system based on eye state detection is presented to detect driver drowsiness. First, Viola-Jones algorithm approach is used to detect the face and eye areas in the proposed method. The detected eye region is classified as closed or open by making use of a machine learning method. Finally, the eye conditions are analyzed at time domain with PERcentage of eyelid CLOsure (PERCLOS) metric and drowsiness conditions are determined by Support Vector Machine (SVM), kNN and decision tree classifiers. The proposed methods tested on 7 real people and drowsiness states are detected at 99.77%, 94.35%, and 96.62% accuracy, respectively.

Research on Lane Detection of Intelligent Vehicle Based on Single Camera

The detection of lane lines is the basis of lane departure warning system, adaptive cruise control and blind spot monitoring system, and its application research has attracted more and more attention from researchers. Based on computer vision, this paper designs a real-time lane detection and recognition algorithm. Firstly, the original image is pre-processed to remove the interference information and reduce the amount of information calculation. Then, according to the principle of edge detection technology and threshold segmentation technology, the possible lane line areas are segmented. Finally, based on the analysis of the lane structure and direction attributes, the lanes that match the attributes in the possible lane area are obtained. The experimental results show that the method has better accuracy, real-time and robustness than the traditional algorithm, and has certain reference significance for the development of auxiliary driving and unmanned driving.

Automatic Detection System for Vehicular Heatstroke Prevention

Many children and animals had died from Vehicular Heatstroke every year. According to the “Not Heatstroke organization,” 882 children have died due to pediatric Vehicular Heatstroke since 1998 in just in the US. Vehicle manufacturers have not given this tragic problem the needed attention despite the massive development of the technology of vehicles. Nowadays, the technology of vehicles has been improved by different types of systems such as Anti-Lock Brakes, Electronic Stability Control, Adaptive cruise control, Lane-departure warning system, and Self-parking which assist and protect the driver's life during the trip. In this paper, a comparison between this system and other vehicular heatstroke detection systems will be executed. An automatic vehicular heatstroke detection system will be designed based on an Infrared Array Sensor. This system will detect any child inside the vehicle by using the Infrared Array Sensor “Grid EYE” as soon as the driver leaves the vehicle. After that, the system will activate the light and horn of the vehicle as an alarm. And then, the system will send an SMS to the driver by GSM signal. Finally, the system will send a signal to turn the air condition on only in case there is no response from the driver.

Automatic Detection System for Vehicular Heatstroke Prevention

Many children and animals had died from Vehicular Heatstroke every year. According to the “Not Heatstroke organization,” 882 children have died due to pediatric Vehicular Heatstroke since 1998 in just in the US. Vehicle manufacturers have not given this tragic problem the needed attention despite the massive development of the technology of vehicles. Nowadays, the technology of vehicles has been improved by different types of systems such as Anti-Lock Brakes, Electronic Stability Control, Adaptive cruise control, Lane-departure warning system, and Self-parking which assist and protect the driver's life during the trip. In this paper, a comparison between this system and other vehicular heatstroke detection systems will be executed. An automatic vehicular heatstroke detection system will be designed based on an Infrared Array Sensor. This system will detect any child inside the vehicle by using the Infrared Array Sensor “Grid EYE” as soon as the driver leaves the vehicle. After that, the system will activate the light and horn of the vehicle as an alarm. And then, the system will send an SMS to the driver by GSM signal. Finally, the system will send a signal to turn the air condition on only in case there is no response from the driver.

Road geometry perception without accurate positioning and lane information

Road geometry perception indicates road direction and drivable area for Lane Keeping System and Lane Departure Warning System in Advanced Driver Assistance Systems or routing and decision-making in Autonomous Driving. High-definition map with accurate positioning or lane information is generally required. However, these two types of information are not always available because of positioning failure or lane mark degradation. In this paper, a road geometry estimation method is proposed without requirement of accurate positioning and lane information. Due to the fact the lane guides the front vehicle, the potential correlation between the relative azimuth of front vehicle and the direction of road is explored. The stable and observable information of front vehicles are exploited, such as the license plate location. A radar-camera fusion system using image processing, and a nonlinear system is then built with Unscented Kalman Filter to estimate the road curvature. Simulation results with Carla demonstrate that the method enhances the road geometry perception by 77.1%, compared with the baseline method. The proposed method can provide reliable road geometry information for autonomous driving when accurate positioning and lane information are unavailable, so that it renders more robust and safer perception for future autonomous driving.

Lane Departure Assessment via Enhanced Single Lane-Marking.

Vision-based Lane departure warning system (LDWS) has been widely used in modern vehicles to improve drivability and safety. In this paper, a novel LDWS with precise positioning is proposed. Calibration strategy is first presented through a 3D camera imaging model with only three parallel and equally spaced lines, where the three angles of rotation for the transformation from the camera coordinate system to the world coordinate system are deduced. Then camera height is calculated compared to the previous works using a measured one with potential errors. A criterion for lane departure warning with only one of the two lane-markings is proposed to estimate both yaw angle and distance between the lane-markings and the vehicle. Experiments show that calibration strategy can be easily set up and achieve an average of 98.95% accuracy on the lane departure assessment.

Deep embedded hybrid CNN–LSTM network for lane detection on NVIDIA Jetson Xavier NX

In recent years, lane detection has become one of the most important factors in the progress of intelligent vehicles. To deal with the challenging problem of low detection precision and real-time performance of most traditional systems, we proposed a real-time deep lane detection system based on CNN Encoder–Decoder and Long Short-Term Memory (LSTM) networks for dynamic environments and complex road conditions. The CNN Encoder network is used to extract deep features from a dataset and to reduce their dimensionality. A corresponding decoder network is used to map the low resolution encoder feature maps to dense feature maps that correspond to road lane. The LSTM network processes historical data to improve the detection rate through the removal of the influence of false alarm patches on detection results. We propose three network architectures to predict the road lane: CNN Encoder–Decoder network, CNN Encoder–Decoder network with the application of Dropout layers and CNN Encoder–LSTM-Decoder network that are trained and tested on a public dataset comprising 12764 road images under different conditions. Experimental results show that the proposed hybrid CNN Encoder–LSTM-Decoder network that we have integrated into a Lane-Departure-Warning-System (LDWS) achieves high prediction performance namely an average accuracy of 96.36%, a Recall of 97.54%, and a F1-score of 97.42%. A NVIDIA Jetson Xavier NX supercomputer has been used, for its performance and efficiency, to realize an Embedded Deep LDWS.

Lane Departure Warning System Based on New Feature Fusion Algorithm and Departure Judgment Rule

Lane Departure Warning System Based on New Feature Fusion Algorithm and Departure Judgment Rule

Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

Modern vehicles rely on a multitude of sensors and cameras to both understand the environment around them and assist the driver in different situations. Lane detection is an overall process as it can be used in safety systems such as the lane departure warning system (LDWS). Lane detection may be used in steering assist systems, especially useful at night in the absence of light sources. Although developing such a system can be done simply by using global positioning system (GPS) maps, it is dependent on an internet connection or GPS signal, elements that may be absent in some locations. Because of this, such systems should also rely on computer vision algorithms. In this paper, we improve upon an existing lane detection method, by changing two distinct features, which in turn leads to better optimization and false lane marker rejection. We propose using a probabilistic Hough transform, instead of a regular one, as well as using a parallelogram region of interest (ROI), instead of a trapezoidal one. By using these two methods we obtain an increase in overall runtime of approximately 30%, as well as an increase in accuracy of up to 3%, compared to the original method.