Driver Attention Research Articles

Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.

Eye Tracking in Driver Attention Research-How Gaze Data Interpretations Influence What We Learn.

Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their purpose. The main idea is to consider not only the intention behind each glance, but to also account for what is relevant in the surrounding scene, regardless of whether the driver has looked there or not. In essence, the old approaches, unaware as they are of the larger context or motivation behind eye movements, have taken us as far as they can. We propose this more integrative approach to gain a better understanding of the complexity of drivers' informational needs and how they satisfy them in the moment.

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

A survey on vision-based driver distraction analysis

Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as well as tremendous economic and societal costs. Driver inattention, either distraction or fatigue, is the major cause among human factors responsible for the crashes. Distracted driving has been getting increasingly severe, and has caused many more crashes than drowsy driving, while the latter has been more extensively studied. Therefore, we are motivated to present a comprehensive survey on vision-based approaches for driver distraction analysis. In the paper, we firstly provide an overview of driver distraction, then introduce the available datasets and explore the various cues for driver behavior distraction analysis. After that, two forms of driver behavior distraction (visual distraction and manual distraction) are analyzed separately. Lastly, we conclude the evolvement and future directions of driver distraction analysis for safe driving. To the best of our knowledge, we are the first to propose driver behavior distraction and analyze it in a hierarchical way.

Modeling Lateral Acceleration on Ramp Curves of Service Interchanges in India: An Instrumented-Vehicle Study

AbstractRamps are a critical component of an interchange, which seek maximum driver attention to satisfy the deceleration and acceleration requirements. To improve safety and articulate significant...

Driver Behavior Classification System Analysis Using Machine Learning Methods

Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers.

Predicting visual attention of human drivers boosts the training speed and performance of Autonomous Vehicles

Predicting visual attention of human drivers boosts the training speed and performance of Autonomous Vehicles

Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios.

Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver’s gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Introduction: With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted. Method: Utilizing DI-related crash data collected from North Carolina for the period 2013–2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset. Results: The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.

Principles and Methods for Evidence-Based Quantification of the Effect of Seat Belt Non-Use in Crash-Related Litigation

Traffic crashes are a common cause of injury and death, and often result from the negligent actions of an inattentive, speeding, or impaired driver. In such cases, a civil legal action may be brought by an injured claimant for compensation for injuries resulting from a crash. Crash-related litigation is defended on various theories, one of which is to raise the issue of contributory negligence when the claimant was not using an available seat belt at the time of the crash, based on the assertion that the claimed injuries would have been avoided or minimized to some degree if the claimant had been restrained. At present, there are no published standards or systematic approach for assessing and quantifying the contribution of seat belt non-use to the cause of a claimant’s specific injury. A reliable medicolegal analysis that addresses whether contributory negligence can be proven in a specific case requires a multidisciplinary approach: First, the nature and severity of the crash must be reconstructed as it affected the vehicle kinetics (engineering) and in turn affected the kinematics of the occupant (biomechanics), next, the injuries must be described and scaled for severity (medicine/pathology), and finally, the risk of the known injuries given the actual circumstances of the crash and occupant (i.e., unbelted) are compared to the risk of the same injuries, and the same crash circumstances, but in the hypothetical scenario in which the claimant is belted. In the present discussion, methods for analyzing the presence and quantifying the degree of contributory negligence for seat belt non-use, suitable for presentation in a medicolegal setting, are described and illustrated with an example from the author’s personal case inventory. A detailed reconstruction of the crash is described, along with the associated occupant kinematics, and the resulting observed injuries. The injuries are then categorized by their anatomical location, type, and severity using Abbreviated Injury Scale designations. Quantification of the injury risk for the actual (unbelted) vs. hypothetical (belted) scenario is based on case-specific analysis of data accessed from a US national crash injury database The difference in risk for the two exposure scenarios can be quantified in terms of either relative risk (a risk ratio) or attributable risk (a risk proportion), with the goal to determine whether the analysis meets the threshold of a relative risk of >2.0, or an attributable risk of 50%, in order to meet the “more probable than not” standard typically required by courts. As a final step in a reliable analysis that exceeds the legal threshold for relevant evidence, the absolute increase in risk is used to quantify the degree to which the claimant’s seat belt non-use contributed to the likelihood of their injuries.

Driver Attention And The Built Environment Initial, Findings From A Naturalistic Driving Study

Maintaining driver attention is critical in multimodal urban spaces where risks to vulnerable users are not borne by the drivers that impose them. However, without an understanding of what it is within the built environment that elicits appropriate driver attention, it will be difficult to reduce the escalating tide of pedestrian fatalities we currently are experiencing. This study uses eye glance data from a naturalistic driving study tabulated for on-task vs. off-task driving and detailed tabulations of environmental variables to identify which features within the built environment have a significant impact on driver attention. An econometric utility model was generated that identified corridor pavement width, reduced drivewayspacing, and linearly aligned street trees as design features that have a positive impact on driver attention.One-way streets, undelineated parking or unstriped bicycle lanes reduce drivers need to attend. Recommendations for maintaining driver attention within urban spaces are included.

Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas.

Bicycling is inexpensive, environmentally friendly, and healthful; however, bicyclist safety is a rising concern. This study investigates bicycle crash-related key variables that might substantially differ in terms of the party at fault and bicycle facility presence. Employing 5 year (2014–2018) data from the Texas Crash Record and Information System database, the effect of these variables on bicyclist injury severity was assessed for San Antonio, Texas, using bivariate analysis and binary logistic regression. Severe injury risk based on the party at fault and bicycle facility presence varied significantly for different crash-related variables. The strongest predictors of severe bicycle injury include bicyclist age and ethnicity, lighting condition, road class, time of occurrence, and period of week. Driver inattention and disregard of stop sign/light were the primary contributing factors to bicycle-vehicle crashes. Crash density heatmap and hotspot analyses were used to identify high-risk locations. The downtown area experienced the highest crash density, while severity hotspots were located at intersections outside of the downtown area. This study recommends the introduction of more dedicated/protected bicycle lanes, separation of bicycle lanes from the roadway, mandatory helmet use ordinance, reduction in speed limit, prioritization of resources at high-risk locations, and implementation of bike-activated signal detection at signalized intersections.

Advanced Driver Assistant System

Road accidents are the most terrifying thing that can happen to a driver. Worst of all, we refuse to learn from our mistakes along the way. The majority of road users are aware of the general rules and safety measures to take while on the road but injuries and crashes are caused by the negligence of road users. The most common cause of accidents and collision is human error. The aim of this project is to automate and improve the safety of vehicles. Lane Departure Warning System (LDWS) and Emergency Driver Assist System (EDAS) are described in this paper. The LDWS uses camera to track lane markers to see if the driver is drifting accidentally. In this project, whenever the vehicle is moving out of the lane, the device gives the driver a warning in the form of audio or visual signal. Whenever the driver's attention deviates from driving activity for a particular interval of time, EDAS alerts the driver.

Effects of All-Night Driving on Selective Attention in Professional Truck Drivers: A Preliminary Functional Magnetic Resonance Study

Fatigue affects multiple aspects of cognitive performance among drivers. However, even after fatigue builds up, some are still able to maintain the level of behavioral performance. To evaluate these adaptations on the neural network level, we utilized functional magnetic resonance imaging (fMRI). Seventeen male professional drivers underwent two fMRI sessions, once while rested and once in a fatigued condition after 10-h of overnight driving. The cognitive task used in the study involved the detection of visual feature conjunctions, namely the shape and the color. There were no significant differences in the task performance between the conditions except for longer reaction times in the fatigued condition. However, we observed substantial differences in the activation patterns during the cognitive task involving selective attention between the conditions. On the global level, we observed a general decrease in activation strength in the fatigued condition, which appeared to be more pronounced in the left hemisphere. On the local level, we observed a (spatially) extended activation of the medial prefrontal regions in the fatigued condition, which reflected increased cognitive control mechanisms compensating for the diminished efficiency of mechanisms involved in meeting task demands.

Investigating the effect of marking and delineation treatments on driver behavior at highway exit gore areas

Highway exit gore areas are common and essential components of highway networks everywhere. Drivers need to navigate, decelerate, and change lanes at exit gore areas through specified and compacted geometry. This makes exit gore areas potentially crash-prone locations. Studies analyzed factors contributing to crashes at exit gore areas. Although marking and delineation techniques can play a major role in enhancing safety, traffic control devices that serve this end were not given sufficient attention in recent research. Furthermore, the currently used marking and delineation treatments are widely different around the world and deploy devices of various forms, shapes, sizes, and colors. The Flemish Agency for Roads & Traffic (AWV) launched a study to explore the feasibility of adding colored and sizeable eye-catching objects, mounted or grounded exactly at the physical nose, to attract driver's attention and improve driving performance. The eye-tracking and driving behavior of 49 Belgian drivers was investigated in a driving simulator. Participants were also queried about the conspicuity of the control devices and their personal preferences. The results were univocal for situations including a guardrail. Using two mounted panels together, i.e., the horizontal and the vertical, scored better on all levels than the horizontal panel alone. The bigger surface size of the traffic control device significantly improved driving performance and was also favored by the participants. Situations without a guardrail lacked such clear results, which were mixed depending on the measure at hand. A larger size of a grounded object-marker, again, improved driver's performance. Findings concerning the colors red or green appeared to favor red, although this was less univocal. These findings are aimed to spark ideas for further research and to assist practitioners and policy-makers in better designing exit gore areas while achieving more consistency and safety.

An insight into crash avoidance and overtaking advice systems for Autonomous Vehicles: A review, challenges and solutions

Emergence of communication technologies made the automotive industries across the globe to embrace Advanced Driver Assistance Systems (ADAS) by considerable investments to ensure accident-free travel, reduction of pollution, fuel conservation. ADAS achieves its goals by integrating complex subsystems such as obstacle avoidance, overtaking advice, lane changing assistance, planning shortest routes, parking assistance, automatic gear shifting, etc., using the emerging technologies. This article emphasizes the road safety aspect of the ADAS by exploring Crash Avoidance and Overtaking Advice (CAOA) subsystems. Existing studies have a noticeable lack of connectivity between various aspects of CAOA subsystems. This review deeply explores and connects CAOA subsystems like road geometries, road debris, obstacle avoidance algorithms powered by Artificial Intelligence (AI), overtaking advice systems, perception challenges of human drivers in various light and weather conditions, driver inattention and misjudgments, vehicle blind-spots, vehicle parameter analysis, performance of vision sensors, in-vehicle computers, driver–vehicle interactions, Vehicle to Infrastructure (V2I) technologies. This article emphasizes the three primary performance metrics of the ADAS, namely accuracy, response time and robustness. Finally, this article discusses a typical functional architecture and gaps identified in existing studies. This article is structured to assist like-minded researchers, who work on CAOA systems for road safety.

Distracted Driving Using Mobile Phone

Approximately one-quarter of all automobile collisions in the United States are thought to be caused by a distracted or inattentive driver. As more wireless communication, entertainment, and driver assistance technologies become available in vehicles, the number of distracted driving accidents is projected to rise. Driver distraction is a major concern in North America, Europe, and Japan when it comes to road safety. The importance of driver distraction as a road safety concern, on the other hand, has just lately been recognized. This study presents an overview of current studies on in-vehicle driver distraction, with an emphasis on mobile phone usage, as this technol-ogy has garnered the most attention in the literature on driver distraction. The impact of in-vehicle gadgets on driving performance is discussed in this review. The adaptive techniques driv-ers use to maintain acceptable driving performance when distracted are discussed, as well as the situations under which these adaptive tactics can fail and how driving performance is harmed when they do. Legislation prohibiting drivers from using their cellphones while driving has had minimal effect, presumably due to a lack of regulation and enforcement. As potential preventive measures to decrease accidents caused by distracted drivers, behavior modification programs, enhanced vehicle safety, and public awareness campaigns have been created.

Validation of the attention-related driving errors scale in novice adolescent drivers

IntroductionMotor vehicle collisions (MVCs) are a leading cause of death among adolescents. Identifying factors that contribute to adolescent MVCs is a pressing public health need. Exogenous (cell phones, passengers, music) and endogenous (stress, worry, mind-wandering) forms of driver inattention account for approximately 78% of all MVCs in the United States. Though both exogenous and endogenous distraction contribute to crash risk, prior work investigating adolescent crash risk has largely focused on exogenous distractors. The Attention-Related Driving Errors Scale (ARDES) is a promising measure assessing individual differences in endogenous driver inattention that has been validated in adult drivers. Its validation in an adolescent sample may prove useful in tailoring future interventions to decrease MVC risk in young drivers. MethodsThis study sought to validate the ARDES in novice adolescent drivers by investigating its underlying factor structure and its relations with self-reported measures of daily inattention, performance-based attention assessments, and a self-report measure of driving behavior. ResultsReplicating earlier work in adults, results suggested ARDES items can be classified according to their operational level of the driving. The ARDES had good internal reliability and construct validity, suggesting it is a valid self-report measure of the propensity for adolescents' attentional errors while driving. DiscussionThe ARDES provides a useful tool for researchers to identify adolescents at greater risk of attentional errors while driving. Future research should use the ARDES to better understand the role of driver inattention in adolescent crash risk.

Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.

Assessment of Community Noise Annoyance due to Transverse Rumble Strips Installation at Residential Areas

Transverse rumble strips (TRS) acts as a traffic calming device to alert inattentive drivers, however, inappropriate design may generate excessive noise level which cause annoyance among the nearby residents. This study attempts to assess community noise annoyance by investigating current TRS designs and characteristics, measuring roadside noise level and traffic characteristics and evaluating noise annoyance due to TRS installation. Middle overlapped (MO), middle layer overlapped (MLO) and raised rumbler (RR) TRS profile types were selected. Noise indices such as A-weighted equivalent continuous sound pressure level (LAeq), sound level exceeding 10% (L10) and 90% (L90) of specified time were measured using sound level meter (SLM) based on ISO 11819 - 1: 2001 Acoustics: Measurement of the influence of road surfaces on traffic noise - Part 1: statistical pass-by (SPB) method. Traffic characteristics such as volume and speed were also measured. Then, community noise annoyance was evaluated in terms of LAeq changes, noise limit comparison by the Department of Environment (DOE) and traffic noise index (TNI) calculation. It was found that there are various TRS profile types with the thickness of only 3mm. Regardless the types, TRS installation has increased the roadside noise level of about 7dBA and exceeded permissible noise limits.