Analyzing drivers' hazard recognition: Precursors to single-vehicle collisions

Extensive driver behavior and performance information provided by real-world video surveillance and sensor data in the SHRP2 Naturalistic Driving Study has enabled the examination of new layers and pathways leading to crash outcomes. We note that the prominence of hazards and the importance of recognizing them vary systematically across single vs. multi-vehicle crashes, and address a fundamental question about safety: why do around three-quarters of drivers involved in single-vehicle crashes not recognize, perceive, or react to the precipitating event (PE)? Using a path-analytic framework through marginal effects, this study investigates factors correlated to recognition of the PE in single-vehicle events, and how these correlations may act as crash precursors. Logit models, accounting for heterogeneity among events and drivers by estimating both fixed and random parameters, quantified correlations among key variables, given a crash or near-crash event (N = 543). The type of PE, roadway environment factors, and driving maneuvers heavily influenced recognition chances. Drivers had a harder time recognizing less conspicuous hazards (e.g. departing the travel way, decreased recognition chances by 48.29%), but seemed better at recognizing prominent hazards (e.g. vehicle losing control, increased recognition chances by 46.71%). In addition, drivers are less likely to recognize PEs when executing less involved driving maneuvers in more relaxed environments, such as daylight (decreased recognition chances by 16.00%), but are more adept in environments that already demand more attention. Recognition reduced the chances of a crash by 12.23%, so we found similar correlations with crash outcome. Future intelligent transportation systems may focus on increasing driver recognition of potential hazards by bringing attention to less conspicuous hazards and less involved driving environments and actions.

Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods

Transportation safety is highly correlated with driving behavior, especially human error playing a key role in a large portion of crashes. Modern instrumentation and computational resources allow for the monitorization of driver, vehicle, and roadway/environment to extract leading indicators of crashes from multi-dimensional data streams. To quantify variations that are beyond normal in driver behavior and vehicle kinematics, the concept of volatility is applied. The study measures driver-vehicle volatilities using the naturalistic driving data. By integrating and fusing multiple real-time streams of data, i.e., driver distraction, vehicular movements and kinematics, and instability in driving, this study aims to predict occurrence of safety critical events and generate appropriate feedback to drivers and surrounding vehicles. The naturalistic driving data is used which contains 7566 normal driving events, and 1315 severe events (i.e., crash and near-crash), vehicle kinematics, and driver behavior collected from more than 3500 drivers. In order to capture the local dependency and volatility in time-series data 1D-Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and 1DCNN-LSTM are applied. Vehicle kinematics, driving volatility, and impaired driving (in terms of distraction) are used as the input parameters. The results reveal that the 1DCNN-LSTM model provides the best performance, with 95.45% accuracy and prediction of 73.4% of crashes with a precision of 95.67%. Additional features are extracted with the CNN layers and temporal dependency between observations is addressed, which helps the network learn driving patterns and volatile behavior. The model can be used to monitor driving behavior in real-time and provide warnings and alerts to drivers in low-level automated vehicles, reducing their crash risk.

Demographic, driving experience, and psychosocial predictors of adolescent distracted driving beliefs

PURPOSE

The current study had three aims: 1) describe distracted driving beliefs among adolescents by various distraction types (i.e., talking on a hands-free/hands-held cell phone, texting or emailing, taking "selfies," and updating/checking social media); 2) examine the factor structure of distracted driving beliefs; and 3) test whether individual difference factors, shown in prior work to be related to distracted driving behavior, significantly predicted factors of distracted driving beliefs.

METHODS

Three hundred seventy-nine high school students enrolled in non-mandatory Driver's Education courses completed surveys of distracted driving beliefs, sensation seeking, and demographics.

RESULTS

A factor analysis revealed four factors of distracted driving beliefs: 1) self-acceptance of interacting with a cell phone while driving; 2) perceived peer acceptance of interacting with a cell phone while driving; 3) perceived threat of distracted driving to personal safety; and 4) self- and peer- acceptance of talking on a cell phone while driving. Adolescents perceived a greater threat to safety and less self- and peer-acceptance of interacting with cell phones while driving (i.e., texting/emailing, updating/posting to social media, taking selfies) than talking on a cell phone while driving. In general, men, those with more driving experience, higher in sensation seeking, and those placing more importance on checking notifications on a phone had riskier beliefs about distracted driving.

CONCLUSION

Findings suggest adolescent distracted driving beliefs are influenced by individual difference factors, providing some knowledge about the motivations for distracted driving. Future work should consider novel strategies for intervening to reduce this common yet extremely dangerous behavior among adolescents.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Neural Correlates of Simulated Driving While Performing a Secondary Task: A Review

Distracted driving consists in performing a secondary task while driving, such as cell-phone conversation. Given the limited resources of the attentional system, engaging in a secondary task while driving increases the risk to have car accidents. The secondary task engagement while driving can depend on or be affected by different factors, including driver's individual characteristics, necessities, environmental conditions, and so forth. In the present work, the neuroimaging studies that investigated the brain areas involved in simulated driving during the execution of a secondary task (visual and overall auditory tasks) were reviewed in light of driving settings. In general, although there are also differences in decrease and increase brain activations across studies, due to the varieties of paradigms used (simulators, secondary tasks and neuroimaging techniques), the dual-task condition (simulated driving plus secondary task), as compared to the simulated driving-alone condition, was generally found to yield a significant shift in activations from occipital to fronto-parietal brain regions. These findings show that when a secondary task is added during driving the neural system redirects attentional resources away from visual processing, increasing the possibility of incorrect, dangerous or risky behavioral responses. The shift of the attentional resources can occur even if driving behavior is not explicitly affected. Limits of the neuroimaging studies reviewed and future research directions, including the need to explore the role of personality factors in the modulation of the neural programs while engaging distracted driving, are briefly discussed.

Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving

OBJECTIVE

Aim of the study was to evaluate the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving.

BACKGROUND

During highly automated driving, it is allowed to engage in NDR tasks temporarily. However, drivers must be able to take over control when reaching a system limit. There is evidence that the type of NDR task has an impact on takeover performance, but little is known about the specific task characteristics that account for performance decrements.

METHOD

Thirty participants drove in a simulator using a highly automated driving system. Each participant faced five critical takeover situations. Based on assumptions of Wickens's multiple resource theory, stimulus and response modalities of a prototypical NDR task were systematically manipulated. Additionally, in one experimental group, the task was locked out simultaneously with the takeover request.

RESULTS

Task modalities had significant effects on several measures of takeover performance. A visual-manual texting task degraded performance the most, particularly when performed handheld. In contrast, takeover performance with an auditory-vocal task was comparable to a baseline without any task. Task lockout was associated with faster hands-on-wheel times but not altered brake response times.

CONCLUSION

Results showed that NDR task modalities are relevant factors for takeover performance. An NDR task lockout was highly accepted by the drivers and showed moderate benefits for the first takeover reaction.

APPLICATION

Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.

Mechanisms behind distracted driving behavior: The role of age and executive function in the engagement of distracted driving

Performing secondary tasks, such as texting while driving, is associated with an increased risk of motor vehicle collisions (MVCs). While cognitive processes, such as executive function, are involved in driving, little is known about the relationship between executive control and willingness to engage in distracted driving. This study investigated the relationship between age, behavioral manifestations of executive function, and self-reported distracted driving behaviors. Executive difficulty (assessed with the BRIEF-A) as well as demographics (age and gender) was considered as possible predictors of engagement in distracted driving behaviors. Fifty-nine young, middle, and older adults self-reported executive difficulty and weekly engagement in distracted driving behaviors. Results revealed that while partially accounted for by age, global executive difficulty was uniquely related to engagement in distracted driving behaviors. Older age was associated with fewer weekly self-reported distracted driving behaviors while higher self-reported executive difficulty was associated with more frequent weekly engagement in distracted behavior. No significant differences were found between young and middle-aged adults on distracted driving behaviors. Findings conclude that distracted driving is a ubiquitous phenomenon evident in drivers of all ages. Possible mechanisms underlying distracted driving behavior could potentially be related to deficits in executive function.

Horse Injury during Non-Commercial Transport: Findings from Researcher-Assisted Intercept Surveys at Southeastern Australian Equestrian Events

Simple Summary

Research on the transportation of horses has largely focused on the movement of horses by commercial livestock carriers. Information on factors associated with horse injuries sustained during private (non-commercial) transportation in small horse trucks and trailers is limited. This study surveyed drivers transporting their horses to equestrian events in southeastern Australia. Information on drivers, travel practice, vehicle characteristics and horse injury was collected. A total of 55/223 (24.7%) participants reported transportation related injuries to their horses. Of these 72% were described as horse associated (scrambling, slipping and horse-horse interaction), 11% due to mechanical failure, and 6% due to driver error. The risk of horse injury was not associated with driver age or gender, or experience. Drivers that answer the telephone whilst transporting horses, were more likely to report a previous injury experience. There was a trend for participants with <8 hours sleep prior to the survey to have previously had a horse injured. There was a modest positive association between increasing trailer age and the number of injuries. The range of trailer models prevented identification of the importance of individual design features. The study highlights the potential for horses to sustain transportation injuries in privately owned vehicles and warrants further study to address this risk to their welfare.

Abstract

Equine transportation research has largely focused on the commercial land movement of horses. Data on the incidence and factors associated with horse injuries during non-commercial transportation (privately owned horse trucks and trailers) is scant. This study surveyed 223 drivers transporting horses to 12 equestrian events in southeastern Australia. Data collected encompassed driver demographics, travel practice, vehicle characteristics, and incidents involving horse injury. Approximately 25% (55/223) of participants reported that their horses were injured during transportation. Of these 72% were owner classified as horse associated (scrambling, slipping and horse-horse interaction), 11% due to mechanical failure, and 6% due to driver error. Horse injury was not significantly associated with driver age, gender, or experience. Participants that answer the telephone whilst driving were more likely to have previously had a horse injured (p = 0.04). There was a trend for participants with <8 hours sleep prior to the survey to have experienced a previous transportation-related injury (p = 0.056). Increased trailer age was associated with a greater number of injury reports (r² = 0.20; p < 0.04). The diversity in trailer models prevented identification of the importance of individual design features. This study highlights the potential for horses to sustain transportation injuries in privately owned vehicles and warrants further study to address this risk to their welfare.

Do as I say, not as I do: Distracted driving behavior of teens and their parents

INTRODUCTION

Driver distraction is an important contributor to crash risk. Teenage driver distraction can be influenced by the attitudes and behaviors of parents. This study examined teens' and their parents' engagement in distracting behavior while driving.

METHOD

Survey data were collected from a national sample of 403 parent-teen dyads using random-digit dialing telephone interviews.

RESULTS

Results demonstrated few parent or teen sex differences in distracting behavior engagement while driving, or in their perceptions of each others' behavior. Parents and teens' frequencies of distracting behavior engagement were positively correlated. Parents' and teens' perceptions of each others' distracting behavior engagement while driving exceeded their own selfreports. Finally, the likelihood that teens reported engaging in distracting behavior while driving was more strongly associated with their perceptions of their parents' distracting behavior than by parents' self reports of their own behavior.

CONCLUSIONS

These results suggest that parents' examples of driving behavior are an important influence on teen driving behavior, but potentially more important are teens' perceptions of their parents' behaviors.

Driving context influences drivers' decision to engage in visual-manual phone tasks: Evidence from a naturalistic driving study

INTRODUCTION

Visual-manual (VM) phone tasks (i.e., texting, dialing, reading) are associated with an increased crash/near-crash risk. This study investigated how the driving context influences drivers' decisions to engage in VM phone tasks in naturalistic driving.

METHOD

Video-recordings of 1,432 car trips were viewed to identify VM phone tasks and passenger presence. Video, vehicle signals, and map data were used to classify driving context (i.e., curvature, other vehicles) before and during the VM phone tasks (N=374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for all driving.

RESULTS

VM phone tasks were more likely to be initiated while standing still, and less likely while driving at high speeds, or when a passenger was present. Lead vehicle presence did not influence how likely it was that a VM phone task was initiated, but the drivers adjusted their task timing to situations when the lead vehicle was increasing speed, resulting in increasing time headway. The drivers adjusted task timing until after making sharp turns and lane change maneuvers. In contrast to previous driving simulator studies, there was no evidence of drivers reducing speed as a consequence of VM phone task engagement.

CONCLUSIONS

The results show that experienced drivers use information about current and upcoming driving context to decide when to engage in VM phone tasks. However, drivers may fail to sufficiently increase safety margins to allow time to respond to possible unpredictable events (e.g., lead vehicle braking).

PRACTICAL APPLICATIONS

Advanced driver assistance systems should facilitate and possibly boost drivers' self-regulating behavior. For instance, they might recognize when appropriate adaptive behavior is missing and advise or alert accordingly. The results from this study could also inspire training programs for novice drivers, or locally classify roads in terms of the risk associated with secondary task engagement while driving.

Social norms and risk perception: predictors of distracted driving behavior among novice adolescent drivers

PURPOSE

Adolescent drivers are at elevated crash risk due to distracted driving behavior (DDB). Understanding parental and peer influences on adolescent DDB may aid future efforts to decrease crash risk. We examined the influence of risk perception, sensation seeking, as well as descriptive and injunctive social norms on adolescent DDB using the theory of normative social behavior.

METHODS

403 adolescents (aged 16-18 years) and their parents were surveyed by telephone. Survey instruments measured self-reported sociodemographics, DDB, sensation seeking, risk perception, descriptive norms (perceived parent DDB, parent self-reported DDB, and perceived peer DDB), and injunctive norms (parent approval of DDB and peer approval of DDB). Hierarchical multiple linear regression was used to predict the influence of descriptive and injunctive social norms, risk perception, and sensation seeking on adolescent DDB.

RESULTS

92% of adolescents reported regularly engaging in DDB. Adolescents perceived that their parents and peers participated in DDB more frequently than themselves. Adolescent risk perception, parent DDB, perceived parent DDB, and perceived peer DDB were predictive of adolescent DDB in the regression model, but parent approval and peer approval of DDB were not predictive. Risk perception and parental DDB were stronger predictors among males, whereas perceived parental DDB was stronger for female adolescents.

CONCLUSIONS

Adolescent risk perception and descriptive norms are important predictors of adolescent distracted driving. More study is needed to understand the role of injunctive normative influences on adolescent DDB. Effective public health interventions should address parental role modeling, parental monitoring of adolescent driving, and social marketing techniques that correct misconceptions of norms related to around driver distraction and crash risk.

Potential distractions and unsafe driving behaviors among drivers of 1- to 12-year-old children

OBJECTIVE

Driver distraction has been identified as a threat to individual drivers and public health. Motor vehicle collisions remain a leading cause of death for children, yet little is known about distractions among drivers of children. This study sought to characterize potential distractions among drivers of children.

METHODS

A 2-site, cross-sectional, computerized survey of child passenger safety practices was conducted among adult drivers of 1- to 12-year-old children who presented for emergency care between October 2011 to May 2012. Drivers indicated the frequency with which they engaged in 10 potential distractions in the past month while driving with their child. Distractions were grouped in 4 categories: (1) nondriving, (2) cellular phone, (3) child, and (4) directions. Information about other unsafe driving behaviors and sociodemographic characteristics was collected.

RESULTS

Nearly 90% of eligible parents participated. Analysis included 570 drivers (92.2%). Non-driving-related and cellular phone-related distractions were disclosed by >75% of participants. Fewer participants disclosed child (71.2%) and directions-related distractions (51.9%). Child age was associated with each distraction category. Cellular phone-related distractions were associated with the child riding daily in the family car, non-Hispanic white, and higher education. Parents admitting to drowsy driving and being pulled over for speeding had over 2 times higher odds of disclosing distractions from each category.

CONCLUSIONS

Distracted driving activities are common among drivers of child passengers and are associated with other unsafe driving behaviors. Child passenger safety may be improved by preventing crash events through the reduction or elimination of distractions among drivers of child passengers.

Medical Aspects of Fitness to Drive

Orthopedic surgeons and other stakeholders need better return-to-driving guidelines for post–orthopedic surgery and trauma patients. The state of the current orthopedic research is in need of better methodology, particularly regarding control over the cognitive domain’s influence on the physical domain. Recommendations need to be based on an individual’s assessment of physical performance rather than the passage of time after surgery. This article highlights the areas of concern for driving research in the orthopedic community and how they can affect study design.

Driver inattention and driver distraction in serious casualty crashes: data from the Australian National Crash In-depth Study

Driver inattention and driver distraction represent a major problem in road safety. Although both are believed to contribute to increased crash risk, there is currently limited reliable information on their role in crashes. The current study used in-depth data from the Australian National Crash In-depth Study to investigate the role of driver distraction and inattention in serious casualty crashes. The sample included 856 crashes from 2000 to 2011, in which at least one party was admitted to hospital due to crash-related injuries. Crashes were coded using a taxonomy of driver inattention that delineates five inattention subtypes: restricted attention, misprioritised attention, neglected attention, cursory attention, and diverted attention (distraction). Approximately 45% of crashes could not be coded due to insufficient information while in an additional 15% the participant indicated the "other driver was at fault" without specifying whether inattention was involved. Of the 340 remaining cases, most showed evidence of driver inattention (57.6%) or possible inattention (5.9%). The most common subtypes of inattention were restricted attention, primarily due to intoxication and/or fatigue, and diverted attention or distraction. The most common types of distraction involved voluntary, non-driving related distractions originating within the vehicle, such as passenger interactions. The current study indicates that a majority of serious injury crashes involve driver inattention. Most forms of inattention and distraction observed are preventable. This study demonstrates the feasibility of using in-depth crash data to investigate driver inattention in casualty crashes.

Mobile Media Use, Multitasking and Distractibility

Portable media devices are ubiquitous and their use has become a core component of many people’s daily experience, but to what effect? In this paper, the authors review research on the ways in which media use and multitasking relate to distraction, distractibility and impulsivity. They review recent research on the effects of media multitasking on driving, walking, work, and academic performance. The authors discuss earlier research concerning the nature of media’s impact on attention and review cognitive and neuropsychological findings on the effects of divided attention. Research provides clear evidence that mobile media use is distracting, with consequences for safety, efficiency and learning. Greater use of media is correlated with higher levels of trait impulsivity and distractibility, but the direction of causality has not been established. Individuals may become more skilled at media multitasking over time, but intervention is currently required to improve the safe and effective use of mobile media.

Assessing the Effects of In-Vehicle Tasks on Driving Performance

New technologies and devices inside the automobile can compete for drivers’ limited attention, raising concerns about driver distraction. This article provides a short overview of some of the issues surrounding in-vehicle tasks, particularly their impact on visual scanning and driving performance. Evidence from a variety of experimental approaches offers key scientific findings that form the basis for taking action to prevent and manage distraction – through the design of products, advanced technology, legislation, and education and training applications.