Miguel A. Perez

Driver crash risk factors and prevalence evaluation using naturalistic driving data

Significance This paper presents findings about the riskiest factors faced by drivers as informed through the first large-scale, crash-only analysis of naturalistic driving data. Results indicate that many secondary tasks or activities, particularly resulting from the use of handheld electronic devices, are of detriment to driver safety. The analysis uses a large naturalistic database comprising continuous in situ observations made via multiple onboard video cameras and sensors that gathered information from more than 3,500 drivers across a 3-y period. The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.

The Distracted Driver Mechanisms, Models, and Measurement

This chapter investigates driver distraction, a pressing road safety issue. First, research findings regarding the demands placed on drivers by the primary driving tasks and various non-driving-related secondary tasks are reviewed. Second, promising theories and models are reviewed for characterizing how driver distraction is caused and how it affects the driving task. Third, a review is provided of current investigation and measurement methods used in distraction research, guidelines, standards, antidistraction devices, and antidistraction legislation. Fourth, the most important implications from this review are summarized for the various stakeholders in the driver distraction debate. And finally, some important issues for future research into driver distraction are discussed, as is the importance of considering driver distraction in the context of an integrated safety vision. Keywords: Driver distraction; Language: en

Investigation of Driver-Infrastructure and Driver-Vehicle Interfaces for an Intersection Violation Warning System

Research was undertaken to design, develop, and evaluate interfaces for a signalized- and stop-controlled violation warning system. Both infrastructure-based warnings (Driver Infrastructure Interface, DII) and vehicle-based warnings (Driver Vehicle Interface, DVI) were considered. The developed interfaces were tested by placing a driver in an instrumented vehicle on a closed test course with a working signalized intersection. The goal of the effort was to determine which DIIs/DVIs were most effective based upon the time to intersection at which the DII/DVI elicited the correct driver response of braking by the stop bar. While the DIIs that were tested were shown to be largely ineffective for violation warning, results showcase the potential of several DVI modalities, by themselves or in combination, to provide effective warnings to a driver violating a signal- or stop-controlled intersection. Furthermore, results indicate that a DVI warning combined with a vehicles enhanced braking capability (brake precharging and panic brake assist) may enhance the range of acceptable DVIs.

Lower torso muscle activation patterns for high-magnitude static exertions: gender differences and the effects of twisting.

Study Design. Surface electromyographic signals were collected from 14 lower torso muscles while participants resisted high-magnitude static trunk moments applied in a variety of directions. Objectives. To obtain a description of muscle activations in response to large moment magnitudes and axial twisting, including levels of agonistic and antagonistic muscle cocontraction. To assess differences in lower torso muscle activation patterns associated with gender and trial repetition. Summary of Background Data. Back pain is associated with mechanical loads in the back. Biomechanical modeling of these loads is facilitated by knowledge of typical muscle activation patterns. Previous efforts in obtaining such data have often limited their scope to low-magnitude exertions or relatively simple scenarios. Methods. Eight male and eight female participants, matched by height and mass, performed static exertions in an apparatus that immobilized their lower body while the activation levels of seven bilateral torso muscles were measured using surface electromyography. Activation patterns were analyzed to assess differences resulting from a variety of factors. Results. No significant differences in activation patterns were found between genders or repetitions, but moment magnitude and direction elicited substantial differential responses. Good repeatability was found between trial repetitions, as indicated by intraclass correlation coefficients (>0.65). Significant synergistic muscle coactivation, large intersubject variability (mean coefficient of variation 82.2%), and consistent levels of antagonism ranging from 10% to 30% maximum voluntary exertions were observed. Conclusions. Individuals of different genders, but similar anthropometry, have comparable muscular reactions to complex torso loads, suggesting similar motor control strategies. Future spine models should consider that the variability in muscle recruitment patterns is larger between subjects than within subjects. High-magnitude exertions, especially those with moment loads in more than one plane, require most muscles to be active (>5%) and moderate levels of antagonism.

The effects of age on crash risk associated with driver distraction

Background Driver distraction is a major contributing factor to crashes, which are the leading cause of death for the US population under 35 years of age. The prevalence of secondary-task engagement and its impacts on distraction and crashes may vary substantially by driver age. Methods Driving performance and behaviour data were collected continuously using multiple cameras and sensors in situ for 3542 participant drivers recruited for up to 3 years for the Second Strategic Highway Research Program Naturalistic Driving Study. Secondary-task engagement at the onset of crashes and during normal driving segments was identified from videos. A case-cohort approach was used to estimate the crash odds ratios associated with, and the prevalence of, secondary tasks for four age groups: 16-20, 21-29, 30-64 and 65-98 years of age. Only severe crashes (property damage and higher severity) were included in the analysis. Results Secondary-task-induced distraction posed a consistently higher threat for drivers younger than 30 and above 65 when compared with middle-aged drivers, although senior drivers engaged in secondary tasks much less frequently than their younger counterparts. Secondary tasks with high visual-manual demand (e.g. visual-manual tasks performed on cell phones) affected drivers of all ages. Certain secondary tasks, such as operation of in-vehicle devices and talking/singing, increased the risk for only certain age groups. Conclusions Teenaged, young adult drivers and senior drivers are more adversely impacted by secondary-task engagement than middle-aged drivers. Visual-manual distractions impact drivers of all ages, whereas cognitive distraction may have a larger impact on young drivers.

Naturalistic Driving Study: Technical Coordination and Quality Control

This report describes the technical coordination and quality control carried out by the Virginia Tech Transportation Institute (VTTI) for the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS). This project encompassed procurement of the data acquisition system (DAS) and all associated installation and driver assessment equipment; coordination of human subjects protections; participant recruitment; training and coordination of the six site contractors that carried out participant enrollment, instrumentation, and data retrieval; data management; data processing; and quality control. From October 2010 through November 2013, the study collected continuous driving information on more than 3,000 light-vehicle drivers, covering about 50 million miles of driving in the six study sites. In this report, potential users of the SHRP 2 NDS data or findings will find a summary of data collection methods and procedures, instrumentation, quality control, and project management.

Chapter 6 – Naturalistic Driving Studies and Data Coding and Analysis Techniques

Publisher Summary This chapter describes the traffic conflict technique and the theory behind the power of instrumented vehicle or naturalistic driving studies, the life cycle of naturalistic driving studies, and powerful analytic techniques that can and have been used with these data. Naturalistic driving data provide powerful tools for safety researchers that incorporate some characteristics of epidemiological data analysis techniques with empirical data analysis techniques. Although these characteristics are very beneficial, they also provide novel new data and analytic methods in which to explore and study driver safety, specifically driver behavior. The life cycle of naturalistic driving studies includes the following: study design and data collection, data preparation and storage, data coding, and data analysis. Each of these steps is complex primarily due to the size and the extent of the data being collected. Naturalistic driving studies typically collect 6–8 gigabytes of video per minute, which can easily result in thousands of hours of video collected, and 6–10 TB of data that must be prepared, stored, coded, and analyzed. Naturalistic driving studies are typically lengthy and resource-intensive but worth the rich, detailed data that can be collected. These types of studies are complex and require extensive planning both prior to data collection and through the entire life cycle of the study to ensure that the initial research objectives are appropriately evaluated. Detailed planning at every step in the life cycle will result in a much easier and efficient data analysis phase of the project.

Compensatory behavior of drivers when conversing on a cell phone

Experimental studies have found that driving degrades when the driver is conversing on a cell phone. Naturalistic driving studies (NDSs), however, have not found conversing on a cell phone to be associated with increased risk of a safety-critical event (SCE). NDSs have found commercial motor vehicle (CMV) drivers to be at decreased SCE risk when conversing on a hands-free cell phone. This study used naturalistic driving data sets to investigate whether driver adaptation took place when drivers of light vehicles and CMVs were conversing on a cell phone. Baseline epochs 30 s prior to cell phone calls were sampled. Drivers’ travel speeds, headways, inclinations to travel in the slowest lane, inclinations to change lanes, and lane-keeping performances were compared. There was no indication that drivers increased their longitudinal safety margins when conversing on a cell phone. Their headways to a lead vehicle did not differ despite CMV drivers significantly increasing their speeds by 4 km/h (2.5 mph) when conversing on a cell phone. However, CMV drivers changed lanes significantly less and light-vehicle drivers unintentionally departed their lanes significantly less when conversing on a handheld cell phone. Overall, the observed performance changes were not substantial. Given that drivers look forward more often when conversing on a cell phone, it is likely that the increased visual attention to the forward roadway may ultimately be why conversing on a cell phone has not been found to increase SCE risk.

A neural network model for predicting postures during non-repetitive manual materials handling tasks

Posture prediction can be useful in facilitating the design and evaluation processes for manual materials handling tasks. This study evaluates the ability of artificial neural network models to predict initial and final lifting postures in 2-D and 3-D scenarios. Descriptors for the participant and condition of interest were input to the models; outputs consisted of posture-defining joint angles. Models were trained with subsets of an existing posture database before predictions were generated. Trained models predictions were then evaluated using the remaining data, which included conditions not presented during training. Prediction errors were consistent across these data subsets, suggesting the models generalised well to novel conditions. The models generally predicted whole-body postures with per-joint errors in the 5°–20° range, though some errors were larger, particularly for 3-D conditions. These models provided reasonably accurate predictions, even outperforming some computational approaches previously proposed for similar purposes. Suggestions for future refinement of such models are presented. The models in this investigation provide a means to predict initial and final postures in commonly occurring manual materials handling tasks. In addition, the model structures provide information about potential lifting strategies that may be used by individuals with particular anthropometry or strength characteristics.

Effects of Haptic Brake Pulse Warnings on Driver Behavior during an Intersection Approach

Intersection crashes account for nearly a quarter of all police reported crashes, and 39% of these result in injury or death. In this experiment, haptic warnings were explored as an alternative to auditory and visual warnings as part of an overall effort to reduce the number of intersection related crashes. The study objective was to determine the haptic brake pulse warning candidate that most often results in the driver successfully stopping for an intersection. Five candidate brake pulse warnings were tested; these varied with respect to length and number of pulses. Significant differences were found between haptic conditions for peak and constant deceleration. Participants receiving the haptic warning were 38 times more likely to stop than those receiving no warning.