The role of looming and attention capture in drivers' braking responses

This study assessed the ability of drivers to detect the deceleration of a preceding vehicle in a simulated vehicle-following task. The size of the preceding vehicles (car, van, or truck) and following speeds (50, 70, or 100 km/h) were systematically varied. Participants selected a preferred following distance by engaging their vehicle's cruise control and when the preceding vehicle began decelerating (no brake lights were illuminated), the participant's braking latency and distances to the lead vehicle were recorded. The experiment also employed a secondary task condition to examine how the attention-capturing properties of a looming vehicle were affected by driver distraction. The results indicated that a looming stimulus is capable of redirecting a driver's attention in a vehicle following task and, as with detection of brake lights, a driver's detection of a looming vehicle is compromised in the presence of a distracting task. Interestingly, increases in vehicle size had the effect of decreasing drivers' braking latencies and drivers engaged in the secondary task were significantly closer to the lead vehicle when they began braking, regardless of the size of the leading vehicle. Performance decrements resulting from the secondary task were reflected in a time-to-collision measure but not in optic expansion rate, lending support to earlier arguments that time-to-collision estimates require explicit cognitive judgements while perception of optic expansion may function in a more automatic fashion to redirect a driver's attention when cognitive resources are low or collision is imminent.     

Determination of pre-impact occupant postures and analysis of consequences on injury outcome. Part I: a driving simulator study

This paper considers pre-impact vehicle maneuvers and analyzes the resulting driver motion from their comfort seating position. Part I of this work consists of analyzing the driver behavior during a crash. The study is conducted using the LAMIH driving simulator and involves 76 participants. The emergency situation is created by a truck emerging from behind a tractor on the opposite side of the road and tearing along the participant. The driver positioning throughout the simulation is recorded via five video cameras allowing view of the front scene, the driver face, feet and pedals, hands on the steering wheel and global lateral view. Data related to braking force, seat pressure, muscular activity for major groups of muscles and actions on the steering wheel are also collected.The typical response to this type of emergency event is to brace rearward into the seat and to straighten the arms against the steering wheel, or, to swerve to attempt to avoid the impacting vehicle. While turning the steering wheel, the forearm can be directly positioned on the airbag module at time of crash which represents a potential injurious situation.These positions are used in Part II to determine scenario of positions for numerical simulation of a frontal collision.     

An evaluation of the general effect of the New Zealand graduated driver licensing system on motorcycle traffic crash hospitalisations

In 1987, New Zealand introduced a comprehensive Graduated Driver Licensing System (GDLS) which was designed to allow new drivers to gain driving experience while being excluded from high risk situations. This study sought to evaluate the impact of the GDLS on motorcycle traffic crashes that resulted in serious injury. Injury crash data were obtained from the New Zealand Health Information Services national public hospital inpatient data files for the years 1978–1994, inclusive. Cases were disaggregated into three age groups, 15–19 years, 20–24 years, and 25 years or older, for which injury trends were obtained. In order to determine whether trends in motorcycle traffic crashes simply followed national trends in other injury events, two non-traffic comparison groups for the main target group (15–19 years) were included. Using time series analyses, the introduction of the GDLS was found to be closely followed by a significant reduction (22%) in motorcycle traffic crash hospitalizations for the 15–19 year age group. An examination of vehicle registration and driver licensure data suggests that the reduction in injury crashes may, largely, be attributable to an overall reduction in exposure to motorcycle riding.     

The effects of on-street parking and road environment visual complexity on travel speed and reaction time

On-street parking is associated with elevated crash risk. It is not known how drivers’ mental workload and behaviour in the presence of on-street parking contributes to, or fails to reduce, this increased crash risk. On-street parking tends to co-exist with visually complex streetscapes that may affect workload and crash risk in their own right. The present paper reports results from a driving simulator study examining the effects of on-street parking and road environment visual complexity on driver behaviour and surrogate measures of crash risk. Twenty-nine participants drove a simulated urban commercial and arterial route. Compared to sections with no parking bays or empty parking bays, in the presence of occupied parking bays drivers lowered their speed and shifted their lateral position towards roadway centre to compensate for the higher mental workload they reported experiencing. However, this compensation was not sufficient to reduce drivers’ reaction time on a safety-relevant peripheral detection task or to an unexpected pedestrian hazard. Compared to the urban road environments, the less visually complex arterial road environment was associated with speeds that were closer to the posted limit, lower speed variability and lower workload ratings. These results support theoretical positions that proffer workload as a mediating variable of speed choice. However, drivers in this study did not modify their speed sufficiently to maintain safe hazard response times in complex environments with on-street parking. This inadequate speed compensation is likely to affect real world crash risk.     

Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections

Drivers’ indecisiveness in dilemma zones (DZ) could result in crash-prone situations at signalized intersections. DZ is to the area ahead of an intersection in which drivers encounter a dilemma regarding whether to stop or proceed through the intersection when the signal turns yellow. An improper decision to stop by the leading driver, combined with the following driver deciding to go, can result in a rear-end collision, unless the following driver recognizes a collision is imminent and adjusts his or her behavior at or shortly after the onset of yellow. Considering the significance of DZ-related crashes, a comprehensive safety measure is needed to characterize the level of safety at signalized intersections. In this study, a novel safety surrogate measure was developed utilizing real-time radar field data. This new measure, called safety surrogate histogram (SSH), captures the degree and frequency of DZ-related conflicts at each intersection approach. SSH includes detailed information regarding the possibility of crashes, because it is calculated based on the vehicles conflicts. An example illustrating the application of the new methodology at two study sites in Virginia is presented and discussed, and a comparison is provided between SSH and other DZ-related safety surrogate measures mentioned in the literature. The results of the study reveal the efficacy of the SSH as complementary to existing surrogate measures.     

Rainfall effect on single-vehicle crash severities using polychotomous response models

As part of the Wisconsin road weather safety initiative, the objective of this study is to assess the effects of rainfall on the severity of single-vehicle crashes on Wisconsin interstate highways utilizing polychotomous response models.Weather-related factors considered in this study include estimated rainfall intensity for 15 min prior to a crash occurrence, water film depth, temperature, wind speed/direction, stopping sight distance and deficiency of car-following distance at the crash moment. For locations with unknown weather information, data were interpolated using the inverse squared distance method. Non-weather factors such as road geometrics, traffic conditions, collision types, vehicle types, and driver and temporal attributes were also considered. Two types of polychotomous response models were compared: ordinal logistic and sequential logistic regressions. The sequential logistic regression was tested with forward and backward formats. Comparative models were also developed for single vehicle crash severity during clear weather.In conclusion, the backward sequential logistic regression model produced the best results for predicting crash severities in rainy weather where rainfall intensity, wind speed, roadway terrain, driver's gender, and safety belt were found to be statistically significant. Our study also found that the seasonal factor was significant in clear weather. The seasonal factor is a predictor suggesting that inclement weather may affect crash severity. These findings can be used to determine the probabilities of single vehicle crash severity in rainy weather and provide quantitative support on improving road weather safety via weather warning systems, highway facility improvements, and speed limit management.     

A tree-structured crash surrogate measure for freeways

In this paper, we propose a novel methodology to define and estimate a surrogate measure. By imposing a hypothetical disturbance to the leading vehicle, the following vehicle’s action is represented as a probabilistic causal model. After that, a tree is built to describe the eight possible conflict types under the model. The surrogate measure, named Aggregated Crash Index (ACI), is thus proposed to measure the crash risk. This index reflects the accommodability of freeway traffic state to a traffic disturbance. We further apply this measure to evaluate the crash risks in a freeway section of Pacific Motorway, Australia. The results show that the proposed indicator outperforms the three traditional crash surrogate measures (i.e., Time to Collision, Proportion of Stopping Distance, and Crash Potential Index) in representing rear-end crash risks. The applications of this measure are also discussed.     

Predicting injury risk with “New Car Assessment Program” crashworthiness ratings

The relationship between crashworthiness ratings produced by the National Highway Traffic Safety Administration's (NHTSA's) New Car Assessment Program (NCAP) and the risk of incapacitating injury or death for drivers who are involved in single-car, fixed-object, frontal collisions was examined. The results are based on 6,405 such crashes from the Motor Vehicle Traffic Accident file of the Texas Department of Highways and Public Transportation. The risk of injury was modeled using logistic regression taking into account the NCAP test results for each individual model of car and the intervening effects of car mass, age of the driver, restraint use, and crash severity. Three measures of anthropometric dummy response, Head Injury Criterion (HIC), Chest Deceleration (CD), and femur load were used to indicate vehicle crash test performance. The results show that there is a significant relationship between the results of the NCAP tests and the risk of serious injury or death in actual single-car frontal accidents. In terms of overall injury, chest deceleration was a better predictor than the Head Injury Criterion. For restrained drivers, crash severity, driver age, and chest deceleration were significant parameters for predicting risk of serious injury or death; the risk of injury decreased as chest deceleration decreased. The results were similar for unrestrained drivers although vehicle mass and femur load were also significant factors in the model. The risk of overall injury decreased as chest deceleration decreased but appeared to decrease as femur load increased.     

Crash involvement of drivers with multiple crashes

A goal for any licensing agency is the ability to identify high-risk drivers. Kentucky data show that a significant number of drivers are repeatedly involved in crashes. The objective of this study is the development of a crash prediction model that can be used to estimate the likelihood of a driver being at fault for a near future crash occurrence. Multiple logistic regression techniques were employed using the available data for the Kentucky licensed drivers. This study considers as crash predictors the driver's total number of previous crashes, citations accumulated, the time gap between the latest two crashes, crash type, and demographic factors. The driver's total number of previous crashes was further disaggregated into the drivers' total number of previous at-fault and not-at-fault crashes. The model can be used to correctly classify at-fault drivers up to 74.56% with an overall efficiency of 63.34%. The total number of previous at-fault crash involvements, and having previous driver license suspensions and traffic school referrals are strongly associated with a driver being responsible for a subsequent crash. In addition, a driver's likelihood to be at fault in a crash is higher for very young or very old, males, drivers with both speeding and non-speeding citations, and drivers that had a recent crash involvement. Thus, the model presented here enables agencies to more actively monitor the likelihood of a driver to be at fault in a crash.     

A comprehensive analysis of factors influencing the injury severity of large-truck crashes

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data – these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.     

Statistical implications of using moving violations to determine crash responsibility in young driver crashes

Traditional methods for determining crash responsibility – most commonly moving violation citations – may not accurately characterize at-fault status among crash-involved drivers given that: (1) issuance may vary by factors that are independent of fault (e.g., driver age, gender), and (2) these methods do not capture driver behaviors that are not illegal but still indicative of fault. We examined the statistical implications of using moving violations to determine crash responsibility in young driver crashes by comparing it with a method based on crash-contributing driver actions. We selected all drivers in police-reported passenger-vehicle crashes (2010–2011) that involved a New Jersey driver <21 years old (79,485 drivers < age 21, 61,355 drivers ≥ age 21). For each driver, crash responsibility was determined from the crash report using two alternative methods: (1) issuance of a moving violation citation; and (2) presence of a driver action (e.g., failure to yield, inattention). Overall, 18% of crash-involved drivers were issued a moving violation while 50% had a driver action. Only 32.2% of drivers with a driver action were cited for a moving violation. Further, the likelihood of being cited given the presence of a driver action was higher among certain driver subgroups—younger drivers, male drivers, and drivers in single-vehicle and more severe crashes. Specifically among young drivers, those driving at night, carrying peer passengers, and having a suspended or no license were more often cited. Conversely, fatally-injured drivers were almost never cited. We also demonstrated that using citation data may lead to statistical bias in the characterization of at-fault drivers and of quasi-induced exposure measures. Studies seeking to accurately determine crash responsibility should thoughtfully consider the potential sources of bias that may result from using legal culpability methods. For many studies, determining driver responsibility via the identification of driver actions may yield more accurate characterizations of at-fault drivers.     

Background music as a risk factor for distraction among young-novice drivers

There are countless beliefs about the power of music during driving. The last thing one would think about is: how safe is it to listen or sing to music? Unfortunately, collisions linked to music devices have been known for some time; adjusting the radio controls, swapping tape-cassettes and compact-discs, or searching through MP3 files, are all forms of distraction that can result in a near-crash or crash. While the decrement of vehicular performance can also occur from capacity interference to central attention, whether or not music listening is a contributing factor to distraction is relatively unknown. The current study explored the effects of driver-preferred music on driver behavior. 85 young-novice drivers completed six trips in an instrumented Learners Vehicle. The study found that all participants committed at-least 3 driver deficiencies; 27 needed a verbal warning/command and 17 required a steering or braking intervention to prevent an accident. While there were elevated positive moods and enjoyment for trips with driver-preferred music, this background also produced the most frequent severe driver miscalculations and inaccuracies, violations, and aggressive driving. However, trips with music structurally designed to generate moderate levels of perceptual complexity, improved driver behavior and increased driver safety. The study is the first within-subjects on-road high-dose double-exposure clinical-trial investigation of musical stimuli on driver behavior.     

Influences of pre-crash braking induced dummy – Forward displacements on dummy behaviour during EuroNCAP frontal crashtest

Combination of active and passive safety systems is a future key to further improvement in vehicle safety. Autonomous braking systems are able to reduce collision speeds, and therefore severity levels significantly. Passengers change their position due to pre-impact vehicle motion, a fact, which has not yet been considered in common crash tests. For this paper, finite elements simulations of crash tests were performed to show that forward displacements due to pre-crash braking do not necessarily increase dummy load levels. So the influence of different pre-crash scenarios, all leading to equal closing speeds in the crash phase, are considered in terms of vehicle motion (pitching, deceleration) and restraint system configurations (belt load limiter, pretensioner). The influence is evaluated by dummy loads as well as contact risk between the dummy and the interior.     

Extended time-to-collision measures for road traffic safety assessment

This article describes two new safety indicators based on the time-to-collision notion suitable for comparative road traffic safety analyses. Such safety indicators can be applied in the comparison of a do-nothing case with an adapted situation, e.g. the introduction of intelligent driver support systems. In contrast to the classical time-to-collision value, measured at a cross section, the improved safety indicators use vehicle trajectories collected over a specific time horizon for a certain roadway segment to calculate the overall safety indicator value. Vehicle-specific indicator values as well as safety-critical probabilities can easily be determined from the developed safety measures. Application of the derived safety indicators is demonstrated for the assessment of the potential safety impacts of driver support systems from which it appears that some Autonomous Intelligent Cruise Control (AICC) designs are more safety-critical than the reference case without these systems. It is suggested that the indicator threshold value to be applied in the safety assessment has to be adapted when advanced AICC-systems with safe characteristics are introduced.     

Serious or fatal driver injury rate versus car mass in head-on crashes between cars of similar mass

This work was performed to determine relations between car mass and driver injuries (serious or fatal) when cars of similar mass crash into each other head-on. This type of crash is examined because it is considered similar in some respects to a barrier crash. Data from the United States Fatal Accident Reporting System (FARS) are used to examine driver fatality likelihood as a function of car mass when cars of similar mass crash into each other. Pedestrian fatalities involving cars of the same mass are used to estimate exposure. Two additional sources of data (State data from North Carolina and New York) are used to generate information on the number of drivers seriously injured or killed per police reported crash when cars of similar mass crash into each other. The present study finds that the likelihood of driver injury (fatal or serious) when cars of similar mass crash into each other increases with decreasing car mass, both for head-on crashes and for crashes in all directions. The study does not address possible mechanisms that might lead to such relations. All the data analyzed reveal a fairly consistent picture--a driver in a 900 kg car crashing head-on into another 900 kg car is about 2.0 times as likely to be seriously injured or killed as is a driver of a 1800 kg car crashing head-on into another 1800 kg car.     

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.     

Estimation of rear-end crash potential using vehicle trajectory data

Recent advancement in traffic surveillance systems has allowed for obtaining more detailed vehicular movement such as individual vehicle trajectory data. Understanding the characteristics of interactions between leading vehicle and following in the traffic flow stream is a backbone for designing and evaluating more sophisticated traffic and vehicle control strategies. This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real time based on the analysis of vehicular movements. The methodology presented in this study consists of two components. The first estimates the probability that a vehicle's trajectory belonging to either ‘changing lane’ or ‘going straight’. A binary logistic regression (BLR) is used to model the lane-changing decision of the subject vehicle. The other component derives crash probability by an exponential decay function using time-to-collision (TTC) between the subject vehicle and the front vehicle. Also, an aggregated measure, crash risk index (CRI) is used in the analysis to accumulate rear-end crash potential for each subject vehicle. The result of this study can be used in developing traffic control and information systems, in particular, for crash prevention.     

Driver age and traffic citations resulting from motor vehicle collisions

This paper is an initial effort to examine whether driver age is related to the likelihood of receiving a traffic citation as a consequence of a crash. All crashes involving two motor vehicles in Wisconsin, 1991 were examined; additional information on any injuries resulting from a crash were obtained from hospital discharge records. Controlling for a variety of driver, vehicle and crash-related factors, these data suggest that the likelihood of receiving a citation is an increasing function of driver age.     

Investigating driver injury severity patterns in rollover crashes using support vector machine models

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.     

The effect of distractions on the crash types of teenage drivers

Teenage drivers are overrepresented in crashes when compared to middle-aged drivers. Driver distraction is becoming a greater concern among this group as in-vehicle devices, opportunities for distractions, and teenage drivers' willingness to engage in these activities increase. The objective of this study was to determine how different distraction factors impact the crash types that are common among teenage drivers. A multinomial logit model was developed to predict the likelihood that a driver will be involved in one of three common crash types: an angular collision with a moving vehicle, a rear-end collision with a moving lead vehicle, and a collision with a fixed object. These crashes were evaluated in terms of four driver distraction categories: cognitive, cell phone related, in-vehicle, and passenger-related distractions. Different driver distractions have varying effects on teenage drivers' crash involvement. Teenage drivers that were distracted at an intersection by passengers or cognitively were more likely to be involved in rear-end and angular collisions when compared to fixed-object collisions. In-vehicle distractions resulted in a greater likelihood of a collision with a fixed object when compared to angular collisions. Cell phone distractions resulted in a higher likelihood of rear-end collision. The results from this study need to be evaluated with caution due to the limited number of distraction related cases available in the U.S. GES crash database. Implications for identifying and improving the reporting of driver distraction related factors are therefore discussed.