Lane departure warning system

In this paper a lane departure detection method is studied and evaluated using the full vehicle dynamics software CarSim. The road curvature is estimated and compared to the vehicle trajectory curvature. The proposed algorithm takes account of the steering dynamics and uses the Time to the Lane Keeping (TLK) as a second risk indicator in order to reduce false alarms and integrate the driver corrections. The used nonlinear model deduced from the vehicle lateral dynamics and a vision system is represented by a T-S fuzzy model. Stability conditions of the fuzzy observer are expressed in terms of linear matrix inequalities (LMI)using unmeasurable premise variables. Simulation results show good efficiency of the method under different driving scenarios.

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience.

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.

Lane feature extraction is one of the key computational steps in lane analysis systems. In this paper, we propose a lane feature extraction method, which enables different configurations of embedded solutions that address both accuracy and embedded systems' constraints. The proposed lane feature extraction process is evaluated in detail using real world lane data, to explore its effectiveness for embedded realization and adaptability to varying contextual information like lane types and environmental conditions.

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.

The study sought to understand the relationship between in-vehicle technologies (IVTs) and self-regulatory behaviors among older drivers. In a large multi-site study of 2990 older drivers, self-reported data on the presence of IVTs and avoidance of various driving behaviors (talking on a mobile phone while driving, driving at night, driving in bad weather, and making left turns when there is no left turn arrow) were recorded. Self-reports were used to identify whether avoidance was due to self-regulation. Hierarchical logistic regressions were used to determine whether the presence of a particular IVT predicted the likelihood of a given self-regulatory behavior after controlling for other factors. Results suggest that the presence of Integrated Bluetooth/Voice Control systems are related to a reduced likelihood of avoiding talking on a mobile phone while driving due to self-regulation (OR = 0.37, 95% CI = 0.29-0.47). The presence of a Navigation Assistance system was related to a reduced likelihood of avoiding talking on a mobile phone while driving (OR= 0.65, 95% CI = 0.50-0.84) and avoiding driving at night due to self-regulation (OR = 0.80, 95% CI = 0.64-1.00). Present findings suggest in-vehicle technologies may differently influence the self-regulatory behaviors of older drivers.

Single-vehicle road departure accidents represent a substantial amount of today’s accidents. This paper presents the design and experimental test of an active steering assistance system that offers two functions: lane departure avoidance by temporary activation and maintaining vehicle heading by permanent activation. Each of the two functions can be used exclusively or in combination. Specific constraints related to each of the two functions are taken into account in the control design of the steering assistance by using LMI (linear matrix inequality) and BMI (bilinear matrix inequality) optimization methods. Consequently, lane overshoot is minimized for assistance activations near the border of the lane even during curve negotiation and robust lane keeping is ensured. Furthermore, the effects on lane keeping performance of simultaneous steering by the driver and the assistance are theoretically quantified. The results are verified using a fully equipped prototype vehicle. The obtai...

A great number of traffic accidents occur during curve negotiation maneuvers. Most of these accidents could be avoided if drivers are provided with information that better guide them through the maneuver. Advanced Driver Assistance Systems (ADAS) such as Curve Speed Warning (CSW) have shown to be effective to improve safety on curve maneuvers by warning drivers of the speed required to make a safe curve maneuver. The effectiveness of such warning systems can potentially improve if the warnings are not only adapted to road and traffic condition but also adapted to individual drivers' behavior. In this study, an Adaptive Curve Speed Warning (ACSW) system is developed that presents drivers a two-level visual and audio warning considering the variation in individual drivers' perception-reaction time (PRT). The warning timing is adjusted according to a reward/punishment function to reinforce safer actions while providing an individualized in-time warning. Next, within a driving simulator environment, drivers' performance using ACSW is compared to a CSW that does not consider PRT variation among drivers. Further, variation in drivers' performance on curve maneuvers is discussed with respect to drivers' approaching speed, variation in PRT, and braking behavior. Results show that drivers' interaction with warning systems varies significantly based on their age and gender. In addition, results show how drivers approaching speed to a curve varies significantly based on road characteristics such as curve advisory speed and curve direction. Results from this study contribute to the development of more intelligent ADAS that could improve drivers' comfort and safety.

A great number of traffic accidents occur during curve negotiation maneuvers. Most of these accidents could be avoided if drivers are provided with information that better guide them through the maneuver. Advanced Driver Assistance Systems (ADAS) such as Curve Speed Warning (CSW) have shown to be effective to improve safety on curve maneuvers by warning drivers of the speed required to make a safe curve maneuver. The effectiveness of such warning systems can potentially improve if the warnings are not only adapted to road and traffic condition but also adapted to individual drivers' behavior. In this study, an Adaptive Curve Speed Warning (ACSW) system is developed that presents drivers a two-level visual and audio warning considering the variation in individual drivers' perception-reaction time (PRT). The warning timing is adjusted according to a reward/punishment function to reinforce safer actions while providing an individualized in-time warning. Next, within a driving simulator environment, drivers' performance using ACSW is compared to a CSW that does not consider PRT variation among drivers. Further, variation in drivers' performance on curve maneuvers is discussed with respect to drivers' approaching speed, variation in PRT, and braking behavior. Results show that drivers' interaction with warning systems varies significantly based on their age and gender. In addition, results show how drivers approaching speed to a curve varies significantly based on road characteristics such as curve advisory speed and curve direction. Results from this study contribute to the development of more intelligent ADAS that could improve drivers' comfort and safety.

Towards understanding the development of driver's mental model of a Lane Departure Warning system while driving Human beings are in a constant struggle against time to conceive, plan, and optimally transform resources to make more and new tasks possible. Advanced Driver Assistance Systems (ADAS) are an outcome of such attempts and are getting increasingly popular. These technology-based support systems are designed to 1) mitigate the impact of variation in human performance by improving situation awareness; 2) augment safety under normal as well as time-critical situations; 3) enhance traffic efficiency; and 4) decrease driver workload. The most common ADAS in current automobiles are Lane Departure Warning (LDW) system / Lane Keeping Assistance (LKA) system, Blind Spot Warning (BSW) system, Distance Control Assist (DCA) / Forward Collision Warning (FCW) system and Full speed range Adaptive Cruise Control (ACC) system with Brake control. ADAS are adept at detecting changes in vehicle behavior and the surrounding environment, and respond promptly. Nevertheless, like the people they are designed to assist, they have operational limitations and boundaries. Depending on the circumstances, they need a few or more than a few conditions to be satisfied to come into play. From a technical

Statistics show that worldwide motor vehicle collisions lead to significant deaths and disabilities as well as substantial financial costs to both society and the individuals involved. Unintended lane departure is a leading cause of road fatalities by the collision. To reduce the number of traffic accidents and to improve driver's safety lane departure warning (LDW), the system has emerged as a promising tool. Vision-based lane detection and departure warning system has been investigated over two decades. During this period, many different problems related to lane detection and departure warning have been addressed. This paper provides an overview of current LDW system, describing in particular pre-processing, lane models, lane detection techniques and departure warning system.

The design of an innovative rear-end collision-warning system was evaluated for effectiveness. The crash scenario involves a lead vehicle not moving (LVNM) in one lane of a straight, dry, paved arterial road and a following vehicle approaching in the same lane. The LVNM has a rearfacing sensor and is equipped with the rear crash-warning system, which allows the LVNM to flash its brake lights or its center high-mounted stop lamp, warning the following vehicle that it is approaching too rapidly. Because this problem is complex, the scope was narrowed, and it was assumed that the driver of the following vehicle always detected the warning after a response time lag and then applied hard braking. The warning algorithm (i.e., selecting the most appropriate warning distance for each approaching vehicle speed) was designed based on trade-offs to maximize the capability of preventing crashes, reduce the frequency of nuisance alarms, and minimize the severity of crashes. The overall measures of effectiveness of the warning design were then evaluated for a vehicle speed distribution that represented a suburban arterial road. The findings suggest that the warning system should be very effective in preventing crashes. The expected number of nuisance alarms was small, and for the very small percentage of vehicles that would crash, the expected crash severity was negligible. Experimental studies and field operational tests would be required to obtain more accurate numerical values for the design parameters. The design of an innovative rear-end collision-warning system is addressed and evaluated for effectiveness. For this first analysis of rear collision, we have narrowed the focus to lead-vehicle-not-moving (LVNM) collisions. In the scenario of interest, the LVNM is equipped with a rear-facing sensor that measures the range and speed of the approaching vehicle. The LVNM is assumed to be equipped with a rear crash-warning system that flashes brake lights or the center high-mounted stop lamp when a warning is triggered.

The design of an innovative rear-end collision-warning system was evaluated for effectiveness. The crash scenario involves a lead vehicle not moving (LVNM) in one lane of a straight, dry, paved arterial road and a following vehicle approaching in the same lane. The LVNM has a rearfacing sensor and is equipped with the rear crash-warning system, which allows the LVNM to flash its brake lights or its center high-mounted stop lamp, warning the following vehicle that it is approaching too rapidly. Because this problem is complex, the scope was narrowed, and it was assumed that the driver of the following vehicle always detected the warning after a response time lag and then applied hard braking. The warning algorithm (i.e., selecting the most appropriate warning distance for each approaching vehicle speed) was designed based on trade-offs to maximize the capability of preventing crashes, reduce the frequency of nuisance alarms, and minimize the severity of crashes. The overall measures of effectiveness of the warning design were then evaluated for a vehicle speed distribution that represented a suburban arterial road. The findings suggest that the warning system should be very effective in preventing crashes. The expected number of nuisance alarms was small, and for the very small percentage of vehicles that would crash, the expected crash severity was negligible. Experimental studies and field operational tests would be required to obtain more accurate numerical values for the design parameters. The design of an innovative rear-end collision-warning system is addressed and evaluated for effectiveness. For this first analysis of rear collision, we have narrowed the focus to lead-vehicle-not-moving (LVNM) collisions. In the scenario of interest, the LVNM is equipped with a rear-facing sensor that measures the range and speed of the approaching vehicle. The LVNM is assumed to be equipped with a rear crash-warning system that flashes brake lights or the center high-mounted stop lamp when a warning is triggered.

A Complete Computer vision system can be divided into two main categories: detection and classification. The Lane detection algorithm is a part of the computer vision detection category and has been applied in autonomous driving and smart vehicle systems. The lane detection system is responsible for lane marking in a complex road environment. At the same time, lane detection plays a crucial role in the warning system for a car when departs the lane. The implemented lane detection algorithm is mainly divided into two steps: edge detection and line detection. In this paper, we will compare the state-of-the-art implementation performance obtained with both FPGA and GPU to evaluate the trade-off for latency, power consumption, and utilization. Our comparison emphasises the advantages and disadvantages of the two systems.

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.

Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simu...

Every year, millions of people die from fatalities on the road. This paper develops a lane departure warning system that will alert the driver when the driver may be veering off the road. Recent advances in Deep learning and Artificial Intelligence have shown that Convolutional Neural Networks can be excellent at extracting and identifying features in an image. However, Convolutional Neural Networks are often run on Expensive GPU's with colossal memory and typically run millions of operations in a second. This is a challenging problem for embedded characterized by limited memory or processing power and a real-time capability. In this paper, a lightweight, robust and low memory architecture is explored to enable its incorporation as an embedded system . The proposed final architecture utilizes a novel semantic regression technique that integrates the accuracy of semantic segregation and the speed of regression. An end-to-end Deep learning system is used which takes images as an inputs and outputs the found lane in one shot. The developed system achieves 91.83% accuracy on Malaysian roads.

Objective: This paper aims to investigate age differences in the effectiveness of advanced warning systems. Background: Understanding of age differences in the effectiveness towards advanced driver assistance systems may help successful introduction of new technology in auto-market. Method: 52 drivers were participated in on-road field experiments. They drove approximately 6.2km on urban road (about 25 minutes) and 9.6km of highway (about 10 minutes) either with or without ADAS (Advanced Driver Assistance Systems) support. After completing the driving experiment, comparisons between the ADAS-supported and non-supported groups were conducted. Results: The results suggested that the FCW (Forward Collision Warning) and the LDW (Lane Departure Warning) significantly impact on average velocity and headway on highway driving, and steering reversal rate on urban driving, respectively. Conclusion: The ADAS are likely to make a positive influence on drivers' behaviors by increasing their safety margins such as lower speed and longer headway distance.

Traffic congestion and safety continue to pose significant challenges in urban environments. In this paper, we introduce the Smart Speed Bump (SSBump), a novel traffic calming solution that leverages the Internet of Things (IoT) and innovative non-Newtonian fluid materials to enhance road safety, optimize emergency response times, and improve the overall driving experience. The SSBump uses IoT sensors to detect and communicate with emergency vehicles, reducing response times by temporarily deflating. These sensors also analyze traffic patterns and inform data-driven decisions. Additionally, the SSBump uses an Oobleck mixture that adapts its behavior based on the velocity of approaching vehicles, resulting in a safer and more comfortable experience for drivers. This study commences with an overview of the prevalent traffic congestion, followed by a discussion on various available options in this domain. Subsequently, the paper explores the advantages of smart speed bumps and their operational mechanisms. Finally, it presents a comprehensive analysis of the results, its challenges, and the prospects of the work. The findings of this research demonstrate the potential of the SSBump system to revolutionize traffic control, emergency response time, and the driving experience in smart cities, making it a game-changing innovation for advanced transportation systems.

This report presents the results of an independent evaluation of the Road-Departure Crash Warning System (RDCW), which is designed to warn drivers when they are drifting out of their lane or about to enter a curve at an unsafe speed. The RDCW lateral-drift-warning subsystem monitors a vehicle’s lane position and lateral speed, and alerts the driver when the vehicle is in danger of departing the road or lane. The RDCW curve-speed-warning subsystem monitors vehicle speed and upcoming road curvature and alerts the driver when the vehicle is in danger of losing control in an upcoming curve. The goal of the RDCW is to improve automotive safety by helping drivers avoid road-departure crashes, but this can only occur if the system is useful and drivers respond to it. The RDCW Field Operational Test (FOT) collected 130,000 km of driving data from 78 participants to evaluate the performance, driver acceptance, and safety benefits of the RDCW. The University of Michigan Transportation Researc...

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Advanced in-vehicle technologies have been proposed as a potential way to keep older adults driving for as long as they can safely do so, by taking into account the common declines in functional abilities experienced by older adults. The purpose of this report was to synthesize the knowledge about older drivers and advanced in-vehicle technologies, focusing on three areas: use (how older drivers use these technologies), perception (what they think about the technologies), and outcomes (the safety and/or comfort benefits of the technologies). Sixteen technologies were selected for review and grouped into three categories: crash avoidance systems (lane departure warning, curve speed warning, forward collision warning, blind spot warning, parking assistance, intersection assistance, merging assistance); in-vehicle information systems (navigation assistance, congestion warning, intelligent speed adaptation); and other systems (adaptive cruise control, automatic crash notification, night...

A La ne-Departure Wa rning Sys tem (LDWS) a nd Adva nce Curve-Warning Sys tem (ACWS) a re cri tical a mong s everal Adva nced Dri ver-As s istance Sys tems (ADAS) functions, having s ignificant potential to reduce cra shes. Generally, LDWS us e different i mage processing or opti cal s canning techniques to detect a lane departure. Such LDWS have s ome limitations such as harsh weather or i rregular l a ne markings ca n i nfluence their performance. Other LDWS use a GPS rece iver wi th a ccess to di gital maps wi th l ane-level resolution to i mprove the s ys tem's efficiency but make the overall system more complex and expensive. In this report, a lane-departure detection method is proposed, whi ch uses a standard GPS receiver to determine the lateral shift of a vehicle by comparing a vehicle's trajectory to a reference road direction wi thout the need of a ny digital maps with l ane-level resolution. This method only needs road-level i nformation from a s tandard digital ma pping database. Furthermore, the system estimates the road curvature and provides advisory s peed for a given curve simultaneously. The fi eld test results s how that the proposed s ystem ca n detect a true l ane departure wi th a n a ccuracy of a l most 100%. Al though n o true lane departure was left undetected, occasional false lane departures were detected about 10% of the ti me when the vehicle did not actually depart i ts lane. Furthermore, system always i ssues the curve warning with an a dvisory s peed at a safe distance well ahead of time.

This paper reports preliminary work on an experimental vehicle and simulators and their initial use towards the development of driver adaptive warning and assistance systems that will be triggered by driver inattention monitor in the Drive Safe national consortium project in Turkey. Determination of driver inattention based on visual, sound and driving input data collected from the driver while driving on a test route or its replication in a simulator environment is conducted by the other research groups in the consortium and is not presented here. The paper first gives information about the Drive Safe project and its goals and concentrates on the construction of a data acquisition vehicle used for driver modelling, preliminary work on simulators used for development and testing in the lab, and initial work on development of warning and assistance systems. The warning systems considered are collision risk warning and rollover risk warning. The assistance systems considered are collision avoidance assistance and dynamic stability enhancement assistance, which includes yaw and roll dynamics stability augmentation. Preliminary results are given in the paper on the experimental vehicle and simulators and driver warning and assistance systems.

World Health Organisation has determined that SouthEast Asian region has the highest road traffic death in 2015 involving motorised 2-3 wheelers. Generally, motorcycles are lack of active safety features that capable to prevent road accident. Therefore, this study aims to design and develop active safety features for a motorcycle to prevent a frontal collision through an audible warning to the motorcyclist and detected object when the collision is about to happen. The system should consist of a simple algorithm with a minimum set of hardware. Based on the design criteria for the system, an audible warning will only be given when all three conditions stated below are met. (1) An object is within stopping distance limit, (2) object heading towards the centre of motorcycle and (3) relative velocity of an object might cause a frontal accident. The system called Detection and Warning System for Motorcycle Vehicular Collision Avoidance (DEWAMCA). It is expected that DEWAMCA can assist currently available Blind Spot Detection System (BSDS) to reduce accident involving motorcycles significantly.

Vision Based Driver Assistance Systems are in-vehicle systems that are designed to increase road safety by helping drivers to become better aware of the road. These systems will provide a better view of road on display to guide the drivers by enhancing the visibility of road in adverse weather conditions, to avoid collisions. The goal of each Driver Assistance System is to make driving easier and safer. We are designing a system, which will give us information about Road markings. Our Motto is to develop an efficient algorithm for Road marking detection, tracking and warning for Vision based Driver Assistance systems in real time. The Algorithm consists of five steps, video grabbing, pre-processing, edge detection, Hough transform and post processing. We are implementing this algorithm to overcome the disadvantages of existing methods.

Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simu...

Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X-coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.

Every year, a large number of persons are killed in highway departure crashes due by driver inattention. Because safety is the primary goal of these systems, lane detecting systems can help prevent accidents. Such systems are designed to detect lane markings and alert the driver if the vehicle is drifting out of its lane. Several lane identification systems have been proposed and effectively proven in recent years. The hybrid median filter was used in this study to provide a noble strategy for lane detecting. Lane detection is used in a variety of intelligent vehicle applications, including lane trip recognition and warning boards, intelligent journey control, and autonomous driving. The majority of previous research has ignored the use of image filtering algorithms.

In order to integrate several time critical warning systems, e.g. Collision Warning and Lane Departure Warning, in the same vehicle one has to deal with the problem of warning management to not overload the driver in critical situations and to make sure that driver's focus is directed to the right place. This paper presents INSAFES integration schemes to ensure these issues, and gives general as well as specific use cases based on warning systems integrated in one of INSAFES demonstrator vehicles. From these use cases are then requirements on warning management derived regarding prioritization schemes. The requirements concludes in a proposed extension of the warning management concepts derived in the AIDE project.

In the following, the main concepts of a road lane model are presented that keeps track of an arbitrary number of lane borders. Information from an existing lane detection device, a gyrosensor, and map data are merged and filtered to create a road model with a desired number of road lines. The model is based on clothoids and continuously provides positions, angles, and curvatures of the border lines of the vehicle's own lane as well as of several neighboring lanes. Particularly on urban roads, in situations with upcoming turning lanes, or when the lane detection system fails to detect road lines, the model can still provide plausible information. This information significantly simplifies the situation analysis in further algorithms that rely on a lane detection system and require detailed information on current road lanes.

In the following, the main concepts of a road lane model are presented that keeps track of an arbitrary number of lane borders. Information from an existing lane detection device, a gyrosensor, and map data are merged and filtered to create a road model with a desired number of road lines. The model is based on clothoids and continuously provides positions, angles, and curvatures of the border lines of the vehicle's own lane as well as of several neighboring lanes. Particularly on urban roads, in situations with upcoming turning lanes, or when the lane detection system fails to detect road lines, the model can still provide plausible information. This information significantly simplifies the situation analysis in further algorithms that rely on a lane detection system and require detailed information on current road lanes.

This study aims to identify the potential adverse driving conditions which result from driver behavior, road surfaces and weather conditions for vehicles during a cruise, and to inform the drivers of the other vehicles moving on the same route. Adverse driving condition scenarios were developed via acceleration data in lateral, longitudinal and vertical directions gathered by using an accelerometer sensor placed at the gravity center of the test vehicles. The drivers were warned through the symbols designed according to the developed scenarios in different shapes and colors, displayed on an information screen showing the position of the vehicle. Three different software programs were used for gathering and evaluating the accelerometer data, storing scenario-specific symbols on the internet and transferring these symbols to the other vehicle information displays. The road tests were performed in conditions present in Turkey. It was observed that the vehicle drivers were alerted with the warning symbols which were designed for dangerous road and driving conditions with a latency of approximately 6s on Google maps which appeared on the driver information screen.

Driver drowsiness is a major cause of severe accidents, many of which involve a single vehicle lane departure. The objective of the experiment described in this paper is to determine the relationships between drowsiness, lane departure events (LDE) and effects of a warning system. While in case of driver distraction the impact of such a warning system can be tested in real traffic, for reasons of safety (and reproducibility), a laboratory-based driving simulator is being used in this project. The experiments were conducted with a cohort of 63 healthy male subjects aged 22 to 27 driving for about 2.5 hrs in a stimuli-deprived scenario with a six-fold repetition under carefully controlled conditions. Several hundreds micro-sleep episodes were identified in the 53 successful trials by electrooculogram and video signal and confirmed by behavioral analysis; more than 800 lane departure warnings (LDW) occurred in the assisted sub-cohort of 17 drivers. A combined analysis of the LDE with and without LDW shows significant reduction in number, time, departure length and out-of-lane area for the assisted subjects. The timing and design of the warning could furthermore prevent almost 85% of the lane departure events caused by sleepiness.

Driver drowsiness is a major cause of severe accidents, many of which involve a single vehicle lane departure. The objective of the experiment described in this paper is to determine the relationships between drowsiness, lane departure events (LDE) and effects of a warning system. While in case of driver distraction the impact of such a warning system can be tested in real traffic, for reasons of safety (and reproducibility), a laboratory-based driving simulator is being used in this project. The experiments were conducted with a cohort of 63 healthy male subjects aged 22 to 27 driving for about 2.5 hrs in a stimuli-deprived scenario with a six-fold repetition under carefully controlled conditions. Several hundreds micro-sleep episodes were identified in the 53 successful trials by electrooculogram and video signal and confirmed by behavioral analysis; more than 800 lane departure warnings (LDW) occurred in the assisted sub-cohort of 17 drivers. A combined analysis of the LDE with and without LDW shows significant reduction in number, time, departure length and out-of-lane area for the assisted subjects. The timing and design of the warning could furthermore prevent almost 85% of the lane departure events caused by sleepiness.

This report summarizes results from the Intelligent Vehicle Initiative (IVI) Road Departure Crash Warning System Field Operational Test (RDCW FOT) project. This project was conducted under a cooperative agreement between the U.S. Dept. of Transportation and the University of Michigan Transportation Research Institute, along with its partners, Visteon Corporation and AssistWare Technologies. Road departure crashes account for 15,000 fatalities annually in the U.S. This project developed, validated, and field-tested a set of technologies intended to warn drivers in real time when the driver was drifting from their lane, and a curve-speed warning system designed to provide alerts to help driver slow down when approaching a curve too fast to safely negotiate the curve This report describes the field operational test of the system and subsequent analysis of the data to address the suitability of similar systems for widespread deployment within the U.S. passenger-vehicle fleet. Two areas were addressed: safety-related changes in driver performance including behavior that may be attributed to the system, and levels of driver acceptance in key areas. Testing used 11 passenger sedans equipped with RDCW and a data acquisition system that compiled a massive set of numerical, video, and audio data. Seventy-eight drivers each drove a test vehicle, unsupervised, for four weeks. The resulting data set captured 83,000 miles of driving, with over 400 signals captured at 10 Hz or faster. Analysis of the data shows that with the RDCW system active, relative to the baseline condition, drivers improved lanekeeping by remaining closer to the lane center and reducing the number of excursions near or beyond the lane edges. In addition, turn signal use increased dramatically. The data, however, were unable to confirm a change in driver's curvetaking behaviors that could have been attributed to the curve speed warning system. Driver acceptance was generally positive in relation to the lateral drift component of the system, with reactions to the curve speed warning system being rather mixed. Many additional results and insights are documented in the report.

Lane departure warning (LDW) is one of the safety innovations in autonomous cars that provides vehicle position monitoring. This technology will alarm if the vehicle moves out of the lane. Lane detection and lane measurement is the main part of LDW. The novelty of this research is the method can measure different lane marks. Very important to know the lane mark that can and cannot be passed. We use semantic segmentation to segment lane mark solid, lane mark dashed, and road. After extracting road and lane marks, we use inverse perspective mapping (IPM) to help calculate the measurement between the car and lane mark. The data is that 374 images were collected from several roads in Makassar City. The model was evaluated using intersection over union (IoU), reaching 79.8% accuracy. The developed system also estimates the measure between the vehicle and lane marks. The lane measurement estimation system's test results were evaluated using the root mean square error (RMSE) method to reach between 0.025254 and 0.134345.

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. INDEX TERMS Lane detection, autonomous vehicle, systematic literature review, geometric modelling, deep learning, machine learning. The associate editor coordinating the review of this manuscript and approving it for publication was Long Xu.

Among the most severe crash scenarios are those caused by driver’s decisions to manoeuvre the vehicle to the adjacent lanes. In most scenarios, drivers’ intentionally change lanes to take over another slower vehicle and preserving the current vehicle speed especially on highway road. The decision may be fatal for drivers of incoming or approaching vehicles which are not aware of the intention and fail to reduce their vehicle speed to avoid lane change collision. Hence, this study proposes a lane change decision aid and warning system which aims to support the driver’s decision prior to performing the lane change on highway road where vehicles are travelling in a single direction. The system implements vehicle-to-vehicle communication (V2V) via long-range (LoRa) communication technology to alert the host vehicle of approaching vehicles and warns the approaching vehicle when a host vehicle intends to change lane. Visual and audible warning will be triggered as precaution mechanism for...

Among the most severe crash scenarios are those caused by driver’s decisions to manoeuvre the vehicle to the adjacent lanes. In most scenarios, drivers’ intentionally change lanes to take over another slower vehicle and preserving the current vehicle speed especially on highway road. The decision may be fatal for drivers of incoming or approaching vehicles which are not aware of the intention and fail to reduce their vehicle speed to avoid lane change collision. Hence, this study proposes a lane change decision aid and warning system which aims to support the driver’s decision prior to performing the lane change on highway road where vehicles are travelling in a single direction. The system implements vehicle-to-vehicle communication (V2V) via long-range (LoRa) communication technology to alert the host vehicle of approaching vehicles and warns the approaching vehicle when a host vehicle intends to change lane. Visual and audible warning will be triggered as precaution mechanism for...

Among the most severe crash scenarios are those caused by driver’s decisions to manoeuvre the vehicle to the adjacent lanes. In most scenarios, drivers’ intentionally change lanes to take over another slower vehicle and preserving the current vehicle speed especially on highway road. The decision may be fatal for drivers of incoming or approaching vehicles which are not aware of the intention and fail to reduce their vehicle speed to avoid lane change collision. Hence, this study proposes a lane change decision aid and warning system which aims to support the driver’s decision prior to performing the lane change on highway road where vehicles are travelling in a single direction. The system implements vehicle-to-vehicle communication (V2V) via long-range (LoRa) communication technology to alert the host vehicle of approaching vehicles and warns the approaching vehicle when a host vehicle intends to change lane. Visual and audible warning will be triggered as precaution mechanism for both host and approaching vehicle drivers. Experiments shows that V2V using LoRa can provide contextual information which are useful to assist drivers in deciding whether to change lane or not on highway use case settings.