Lane Departure Research Articles

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.

Impacts of advanced driver assistance systems on commercial truck driver behaviour performance using naturalistic data

Traffic crashes tend to be more serious when commercial trucks are involved. And an increasing number of commercial vehicles are equipped with advanced driver assistance systems (ADAS) for traffic safety improvement. To this end, this study investigated the impacts of ADAS with respect to forward collision warning (FCW), urban forward collision warning (UFCW), lane departure warning (LDW), headway monitoring and warning (HMW), and speed limit indicator (SLI) on commercial truck drivers' behaviours using naturalistic data. Participants experienced two different test scenarios with two different ADAS warning configurations: Visual and audio alerts in the inactive state and in the active state. After activating alerts, 62%, 35%, 69%, 81% and 73% of drivers received less warnings in terms of FCW, UFCW, LDW, HMW and SLI respectively; the average of FCW, UFCW, LDW, HMW and SLI respectively declined 22%, 13%, 28%, 45% and 15%, but median changed little except for LDW and HMW. Then, the further analysis results by the one-way analysis of variance (ANOVA) method revealed that all warnings except UFCW had positive effect on commercial truck drivers' behaviour performance. The findings provide important references for commercial truck driver training and supervision in the age of advanced technology.

The effect of time on task, sleep deprivation, and time of day on simulated driving performance

Sleep deprivation and time of day have been shown to play a critical role in decreasing ability to sustain attention, such as when driving long distances. However, a gap in the literature exists regarding external factors, such as workload. One way to examine workload is via modulating time on task. This study investigated the combined effect of sleep deprivation, time of day, and time on task as a workload factor on driving performance. Twenty-one participants (18–34 years, 10 females) underwent 62 h of sleep deprivation within a controlled laboratory environment. Participants received an 8-h baseline and 9.5-h recovery sleep. Every 8 h, participants completed a Psychomotor Vigilance Task (PVT), Karolinska Sleepiness Scale (KSS), 30-min monotonous driving task and NASA-Task Load Index (TLX). Driving variables examined were lane deviation, number of crashes, speed deviation and time outside the safe zone. Workload was measured by comparing two 15-min loops of the driving track. A mixed model ANOVA revealed significant main effects of day and time of day on all driving performance measures (p < .001). There was a significant main effect of workload on lane deviation (p < .05), indicating that a longer time on task resulted in greater lane deviation. A significant main effect of day (p < .001) but not time of day for the NASA-TLX, PVT and KSS was found. Time on task has a significant further impact on driving performance and should be considered alongside sleep deprivation and time of day when implementing strategies for long-distance driving.

Driving Robot for Reproducible Testing: A Novel Combination of Pedal and Steering Robot on a Steerable Vehicle Test Bench

Shorter development times, increased standards for vehicle emissions and a greater number of vehicle variants result in a higher level of complexity in the vehicle development process. Efficient development of powertrain and driver assistance functions under comparable and reproducible operating conditions is possible on vehicle test benches. Yet, the realistic simulation of real driving environments on test benches is a challenge. Current test procedures and new technologies, such as Real Driving Emission tests and Autonomous Driving, require a reproducible and even more detailed simulation of the driving environment. Due to this, the simulation of curve driving in particular is gaining in importance. This results from its significant influence on energy consumption and Autonomous Driving functions with lateral guidance, such as lane departure and evasion assistance. Reproducibility can be additionally increased by using a driving robot. At today’s vehicle test benches, pedal and shift robots are predominantly used for longitudinal dynamic tests in the performed test procedures. In order to meet these new test automation requirements for vehicle test benches, the cooperative operation of pedal and steering robots is needed on a test bench setup suitable for this purpose. In this publication, the authors present the setup of a vehicle test bench to be used in automated and reproducible vehicle-in-the-loop tests during steering events. The focus is on the test-bench-specific setup with steerable front wheels, the actuators for simulating the wheel steering torque around the steering axle and the robots used for pedals and steering wheel. Results from various test series are presented and the potential of the novel test environment is shown. The results are reproducible in various test series due to the closed-loop operation without human driving influences at the test bench.

On-road trust and perceived risk in Level 2 automation

To encourage appropriate use of driving automation, we need to understand and monitor driver’s trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events.30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined.Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving.Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation.At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.

Investigation of Inlaid Pavement Marker Performance and Safety Effectiveness

The objectives of this study were to assess inlaid pavement marker (IPM) performance and estimate their safety effectiveness in St. Louis, Missouri. IPM performance was evaluated through a count of marker presence and a feedback survey from participants who viewed dry and wet night videos of IPM sections in the St Louis area. The safety effectiveness of IPMs was evaluated using a rigorous state-of-the-art empirical Bayes (EB) before–after crash analysis. The marker presence assessment, unfortunately, did not provide any conclusive trends in IPM performance. Newer IPM sections had higher percentages of missing markers than older sections, and there were too many unknown variables to determine the source of the unexpected differences. The unanimous conclusion of the nighttime video visibility survey was that drivers and passengers traveling on a wet night feel that IPMs are very important to the visibility of the roadway’s lane lines. For the EB analysis, installation and reference site data were used to examine the effects for specific crash types, including total, fatal and injury, wet pavement, nighttime, nighttime wet pavement, lane departure, wet pavement lane departure, and nighttime lane departure. Based on the aggregate results, IPMs, when installed with pavement resurfacing, significantly reduce all crash types examined.

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.

LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor

Modern vehicles are equipped with various driver-assistance systems, including automatic lane keeping, which prevents unintended lane departures. Traditional lane detection methods incorporate handcrafted or deep learning-based features followed by postprocessing techniques for lane extraction using frame-based RGB cameras. The utilization of frame-based RGB cameras for lane detection tasks is prone to illumination variations, sun glare, and motion blur, which limits the performance of lane detection methods. Incorporating an event camera for lane detection tasks in the perception stack of autonomous driving is one of the most promising solutions for mitigating challenges encountered by frame-based RGB cameras. The main contribution of this work is the design of the lane marking detection model, which employs the dynamic vision sensor. This paper explores the novel application of lane marking detection using an event camera by designing a convolutional encoder followed by the attention-guided decoder. The spatial resolution of the encoded features is retained by a dense atrous spatial pyramid pooling (ASPP) block. The additive attention mechanism in the decoder improves performance for high dimensional input encoded features that promote lane localization and relieve postprocessing computation. The efficacy of the proposed work is evaluated using the DVS dataset for lane extraction (DET). The experimental results show a significant improvement of $5.54\%$ and $5.03\%$ in $F1$ scores in multiclass and binary-class lane marking detection tasks. Additionally, the intersection over union ($IoU$) scores of the proposed method surpass those of the best-performing state-of-the-art method by $6.50\%$ and $9.37\%$ in multiclass and binary-class tasks, respectively.

Exploring the impact of seasonal weather factors on frequency of lane-departure crashes in Maine

Lane departure crashes account for approximately 34% of all roadway crashes and over 70% of all roadway fatalities in Maine. Despite an 18% decrease in average daily traffic volume, the half of the year with colder weather, from November to April, comprises over 64% of the yearly lane-departure crashes. The purpose of this study is to explore to what extent seasonal (i.e., winter vs. non-winter) and monthly weather variations impact lane departure crashes on rural Maine roads. We used a negative binomial model with panel data to analyze monthly crashes on Interstates, minor arterials, major collectors, and minor collectors from 2015 to 2019 for winter and non-winter periods. The data include monthly average daily traffic, geometric characteristics, and weather variables. The research results indicate that the seasonal variability as reflected in various weather variables significantly impact the frequency of lane-departure crashes during the winter period. The marginal effect analysis shows that as the number of days with more than 1 inch of snowfall, or rainfall increases by 1%, the average number of lane-departure crashes increases approximately by 0.51% and 0.09% on Interstate roadways, respectively.

Stress-level and attentional functions of experienced and novice young adult drivers in intersection-related hazard situations

The purpose of this study is to determine the differences in stress-level and attentional functions between experienced and novice drivers during intersection-related hazard situations. A simulation experiment was conducted on 21 licensed drivers (15 males and 6 females, mean age 27.86 ± 3.32), categorized into two groups (experienced and novice) based on the frequency and length of their driving experience. The participants were asked to drive in the same lane at a constant speed for 9 randomized trials with three different conditions (no hazard, low hazard, high hazard). Driving performance, ECG, and eye-tracking data were collected throughout the whole session and a subjective questionnaire measuring perceived stress and attention load was administered after every trial. The result showed that although driving experience did not influence both speed and lane deviation, driving performance tends to deteriorate with increased hazard levels. Also, it was found that experienced drivers exhibited greater stress during intersection-related hazard situations than novices. However, regardless of the heightened stress, experienced drivers were faster in terms of attending to the hazard-related stimulus and showed proportional attention allocation to both the primary task and hazard-related stimulus. Overall, the results suggest that experienced drivers exhibited more changes in stress levels and attention load during high hazard situations than novice drivers. The results of this study may be applied in designing more effective training modules and driver support systems that would help drivers specifically during intersection-related hazard situations.

How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas

Traffic crashes are among the leading causes of injuries and fatalities worldwide. The main assumption of this study is that traffic crash rates, injury severity, and driving behaviors differ by the driver’s gender. Utilizing ten years (2011–2020) of data from the Texas Crash Record and Information System database, this study investigates how some of the most prominent driving behaviors leading to crashes and severe injuries (distracted driving, speeding, lane departure, and driving under influence) vary by gender in San Antonio, Texas. The spatial distribution of crashes associated with these driving behaviors by gender is also investigated, as well as the influence of some environmental and temporal variables on crash frequency and injury severity. This study adopted bivariate analysis and logistic regression modeling to identify the effect of different variables on crash occurrence and severity by gender. Male drivers were more likely to be involved in a speeding/DUI/lane departure-related crash and subsequent severe injuries. However, female drivers were slightly more associated with distracted-driving crashes and subsequent injuries. Nighttime, interstate/highway roads, the weekend period, and divider/marked lanes as the primary traffic control significantly increased the crash and injury risk of male drivers. Driving behavior-related crashes were mostly concentrated on some interstate road segments, major intersections, and interchanges. The results from this study can be used by authorities and policy-makers to prioritize the use of limited resources, and to run more effective education campaigns to a targeted audience.

Investigating the effect of contextual factors on driving: An experimental study

Alongside human factors, contextual factors are believed to have an ongoing and complex impact on driving outcomes. However, how and to what extent the components of context influence driving outcomes (e.g. rule violations, crash, stress, fatigue) are far beyond full understanding. The purpose of this study is to explore the effects of weather condition, lighting condition and traffic density on driving outcomes. Thirty-six volunteers were enrolled to participate into a driving simulator-based experiment. Each participant was required to complete twelve trials of simulated driving under different sets of scenarios. Driving outcome was measured by five dependent variables: frequency of speeding, frequency of lane deviations, number of correct sign recognition, completion time and workload. The results showed the frequency of speeding was significantly affected by weather condition, lighting condition and traffic density. Lighting condition had a significant effect on number of correct sign recognition. Weather condition, lighting condition and traffic density had significant effects on task completion time. Weather condition and lighting condition had significant effects on driver’s workload. The implications of the results could help traffic safety professionals better understand the risk factors that may lead to human errors during driving. Practically, countermeasures could be inspired and developed to mitigate the adverse impacts brought by driving context to minimum.

Influence of cannabis use history on the impact of acute cannabis smoking on simulated driving performance during a distraction task

Objective: This research explores the driving performance of people who use cannabis daily or occasionally during distraction tasks performed following acute cannabis use. Methods: Healthy adults aged 25 to 45 years with different cannabis usage histories were recruited to participate in a within-subjects controlled experiment using a car-based driving simulator. Participants were classified as having daily use (n = 31), occasional use (1 or 2 times per week; n = 24), or no-use (n = 30). Participants completed a practice drive followed by four 5–10 minute driving scenarios during the baseline period. Participants then smoked self-procured cannabis flower ad libitum for up to 15 minutes. Thirty minutes later, they completed four additional 5–10 minute scenarios. Scenarios were paired according to difficulty and randomized across the baseline and post-use periods. Each scenario contained between 0 and 3 repetitions of a distraction task where the participant was prompted by an audio message to select an app from a 4 × 5 grid displayed on a mounted tablet, a step that would require briefly looking away from the roadway. Measures of driving performance (lane departures, standard deviation of lateral position) were assessed during the five-second period following the audio trigger and analyzed using generalized linear mixed models. Results: Those with a pattern of occasional use were significantly more likely to experience a lane departure during distraction periods after acute cannabis use relative to baseline (OR = 3.71, p = 0.04, CI = 1.04, 13.17), while those with daily use did not exhibit a similar increase (OR = 1.56, p = 0.43, CI = 0.52, 4.64). Changes in departure risk were significantly greater for the occasional use group compared to no-use (p = 0.02), but not for the daily use group compared to no-use (p = 0.18). However, following acute use, those who use daily exhibited decreases in speed relative to baseline in comparison to the changes observed in the no-use group (p = 0.02), while differences between occasional and no-use did not reach statistical significance (p = 0.052). Differences in standard deviation of lateral position were not statistically significant, likely due to the short duration of tasks. Conclusions: These results find the largest potential safety concerns associated with a pattern of occasional use, who displayed an increase in lane departures after acute cannabis smoking. Those in the daily use group decreased their speed, which may be interpreted as compensation for drug effects. Further research is needed to understand the effects during longer and more complex secondary tasks.

A Novel Fast Responding Driver Assistance Technique with Efficient Lane Detection and Collision Avoidance Using Dynamic Feature Extraction in Any Environment

Road accidents caused by a driver's irresponsibility while driving is becoming increasingly common. Furthermore, if lanes are lacking on the road, even well-trained drivers may find it difficult to keep the lane while driving in low-light conditions. As a result, it is critical to design trustworthy, precise, and efficient mechanisms in the vehicle system that aid the driver in the event of a road collision. Almost every country in the world is attempting to conquer this greatest difficulty. The topic study article focuses on the crossroads and presents a more authentic and efficient diver aid system strategy in terms of lane departure alarm even when lanes are missing by taking prior lane patterns into account. In addition, research is being conducted in order to provide a speedy collision warning with nearly no false alarms. This study develops a fast-response spatial domain approach for detecting lanes on highways, and if lane markers are missing, virtual lanes are constructed using a novel suggested algorithm. In addition, for vehicle collision avoidance, the system estimates the distance, velocity, and direction from the frontal vehicle to itself. The suggested approach is evaluated on real-time videos in all environmental circumstances such as poor or bright sunlight, rain, and twilight, as well as on different road geometries such as straight and curving at various vehicle speeds. In every situation, the system has achieved more than 98.7 percent accuracy. Even if there are no markings on road, the system provides more accurate and reliable experimental results. Finally, the results are compared to several existing algorithms based on accuracy, precision, recall, and F1 score, along with processing time.

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.

Advanced High-Speed Lane Keeping System of Autonomous Vehicle with Sideslip Angle Estimation

An advanced LKS (lane keeping system) for use on curving roads is presented to maintain autonomous vehicle driving within the target lane, without unintentional lane departure. There are the following two main objectives in designing this system: one is performing perfect lane keeping and the other is ensuring the dynamic stability of the vehicle, especially when driving on a curving and low-friction road with time-varying high speed. In this paper, a combined vehicle model, consisting of a lane keeping model and a vehicle lateral dynamic model, is firstly introduced. Then, a novel adaptive-weight predictive controller is used to calculate the desired steering angle and the additional yaw moment which provide coordinated control forlane keeping and dynamic stability control. Meanwhile, a square-root cubature Kalman filter-based vehicle sideslip angle observer, with a strong tracking theory modification (ST-SRCKF), is established to estimate the sideslip angle during the driving process. Finally, HIL (hardware-in-the-loop) tests and field tests are constructed, and the results show the effectiveness of our proposed LKS controller and ST-SRCKF sideslip angle estimation.

Tire Track Identification: A Method for Drivable Region Detection in Conditions of Snow-Occluded Lane Lines

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Today’s Advanced Driver Assistance Systems (ADAS) predominantly utilize cameras to increase driver and passenger safety. Computer vision, as the enabler of this technology, extracts two key environmental features: the drivable region and surrounding objects (e.g., vehicles, pedestrians, bicycles). Lane lines are the most common characteristic extracted for drivable region detection, which is the core perception task enabling ADAS features such as lane departure warnings, lane-keeping assistance, and lane-centering. However, when subject to adverse weather conditions (e.g., occluded lane lines) the lane line detection algorithms are no longer operational. This prevents the ADAS feature from providing the benefit of increased safety to the driver. The performance of one of the leading computer vision system providers was tested in conditions of variable snow coverage and lane line occlusion during the 2020-2021 winter in Kalamazoo, Michigan. The results show that this computer vision system was only able to provide high confidence detections in less than 1% of all frames recorded. This is an alarming result, as 21% of all crashes in the U.S. are weather-related. To increase the capabilities of ADAS when snow-occlusions are present, a tire track identification system was developed by comparing various supervised machine learning models. A custom dataset was collected using the Energy Efficient and Autonomous Vehicles lab’s research platform from Western Michigan University. A data preparation pipeline was implemented to label tire tracks and train the machine learning models. The best model achieved high confidence detections of tire tracks in 83% of all frames of which tire tracks were present, an 82% increase in detections than the leading computer vision system provider.&lt;/div&gt;&lt;/div&gt;

Teen driver crashes potentially preventable by crash avoidance features and teen-driver-specific safety technologies

Introduction: Vehicle technologies have the potential to help address the disproportionate crash risk that teen drivers face. While crash avoidance features benefit the general population, several address crash scenarios for which teen drivers are particularly at risk, such as rear-end and lane-drift crashes. Other emerging technologies have been designed for teen drivers by addressing certain crash or injury risk factors associated with risky driving behavior, such as speeding or not wearing a seat belt. Methods: Using nationwide U.S. crash data from 2016 to 2019, this study examined the maximum potential safety benefits of three currently available crash avoidance features (front crash prevention, lane departure prevention, and blind spot monitoring) and three teen-driver-specific technologies (speeding prevention, extended seatbelt reminders and interlocks, and nighttime curfew violation alerts). Results: Teen-driver-specific features have the largest potential for reducing teen driver injuries and fatalities, followed by lane departure prevention, front crash prevention, and blind spot monitoring; however, altogether these technologies have the potential to prevent 78% of teen driver fatalities, 47% of injured teen drivers, and 41% of crashes involving teen drivers. Conclusions: Crash avoidance features and teen-driver-specific vehicle technologies appear to address different risk factors and crash scenarios, which emphasizes the importance of utilizing both types of safety features to reduce the crash risk of teen drivers. Practical applications: Wider acceptance, accessibility, and use of these technologies are needed for their safety potential to be realized. More manufacturers should offer and advertise teen-driver-specific technology suites that integrate crash avoidance systems and safety features that address risky driving behavior. While this study shows the maximum potential safety benefits of these technologies, further research is needed to understand the behavioral implications as teens learn to drive with these features.

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.

Lane departure prediction based on closed-loop vehicle dynamics

An automated driving system should have the ability to supervise its own performance and to request human driver to take over when necessary. In the lane keeping scenario, the prediction of vehicle future trajectory is the key to realize safe and trustworthy driving automation. Previous studies on vehicle trajectory prediction mainly fall into two categories, that is, physics-based and manoeuvre-based methods. Using a physics-based methodology, this article proposes a lane departure prediction algorithm based on closed-loop vehicle dynamics model. We use extended Kalman filter to estimate the current vehicle states based on sensing module outputs. Then, a Kalman Predictor with actual lane keeping control law is used to predict steering actions and vehicle states in the future. A lane departure assessment module evaluates the probabilistic distribution of vehicle corner positions and decides whether to initiate a human takeover request. The prediction algorithm is capable to describe the stochastic characteristics of future vehicle pose, which is preliminarily proved in simulated tests. Finally, the on-road tests at speeds of 15–50 km/h further show that the proposed method can accurately predict vehicle future trajectory. It may work as a promising solution to lane departure risk assessment for automated lane keeping functions.