Driver secondary tasks in Germany: using interviews to estimate prevalence

Secondary tasks while driving are frequently found in different types of studies from all over the world. For a profound understanding of secondary tasks’ impact on road safety it is essential to know in detail what kind of tasks drivers are doing in which situations. In contrast to costly observational studies, interviews may be a suitable access to these data if reporting biases are minimized. In 2009, 289 drivers were interviewed in face-to-face interviews on German motorway service areas as well as in the city of Braunschweig about their secondary task engagement in the last 30 min of driving. Five groups of drivers were examined: (1) truck drivers at the motorway (N = 90), (2) car drivers on private trips at the motorway (N = 71), (3) car drivers on business trips at the motorway (N = 29), (4) car drivers on private trips in town (N = 85), (5) car drivers on business trips in town (N = 12). The pattern and frequency of engagement in secondary tasks differed between these groups. Overall, about 80% of all drivers conducted one to three secondary tasks. Thus, secondary task engagement is a serious issue in Germany and accident studies are needed to estimate drivers’ risk.     

How operators make use of wide-choice adaptable automation: observations from a series of experimental studies

The present paper is concerned with the design of adaptable automation. It analyses the aggregated data from five experiments (N = 154) to examine which automation levels operators prefer, how often they switch between them, and whether performance is associated with frequent switching or the automation levels being used. Using wide-choice adaptable automation (i.e. up to six levels were offered), the experiments were conducted using a PC-based simulation of a complex work environment. The results showed that about 95% of operators had a clearly preferred automation level, which they used for more than 50% of the time. They strongly preferred intermediate automation levels over levels at the higher and lower ends of the scale. Most operators switched rarely between levels and when they did, they usually made small adjustment rather than large changes. Several implications for the design of adaptable automation were derived from the empirical data.     

Event-related potentials and secondary task performance during simulated driving

Inattention and distraction account for a substantial number of traffic accidents. Therefore, we examined the impact of secondary task performance (an auditory oddball task) on a primary driving task (lane keeping). Twenty healthy participants performed two 20-min tests in the Divided Attention Steering Simulator (DASS). The visual secondary task of the DASS was replaced by an auditory oddball task to allow recording of brain activity. The driving task and the secondary (distracting) oddball task were presented in isolation and simultaneously, to assess their mutual interference. In addition to performance measures (lane keeping in the primary driving task and reaction speed in the secondary oddball task), brain activity, i.e. event-related potentials (ERPs), was recorded. Performance parameters on the driving test and the secondary oddball task did not differ between performance in isolation and simultaneous performance. However, when both tasks were performed simultaneously, reaction time variability increased in the secondary oddball task. Analysis of brain activity indicated that ERP amplitude (P3a amplitude) related to the secondary task, was significantly reduced when the task was performed simultaneously with the driving test. This study shows that when performing a simple secondary task during driving, performance of the driving task and this secondary task are both unaffected. However, analysis of brain activity shows reduced cortical processing of irrelevant, potentially distracting stimuli from the secondary task during driving.     

The effects of cognitively demanding dual-task driving condition on elderly people’s driving performance; Real driving monitoring

BackgroundUsing in-vehicle audio technologies such as audio systems and voice messages is regarded as a common secondary task. Such tasks, known as the sources of non-visual distraction, affect the driving performance. Given the elderly drivers’ cognitive limitations, driving can be even more challenging to drivers. The current study examined how listening to economic news, as a cognitively demanding secondary task, affects elderly subjects’ driving performance and whether their comprehension accuracy is associated with these effects.MethodsParticipants of the study (N = 22) drove in a real condition with and without listening to economic news. Measurements included driving performance (speed control, forward crash risk, and lateral lane position) and task performance (comprehension accuracy).ResultsThe mean driving speed, duration of driving in unsafe zones and numbers of overtaking decreased significantly when drivers were engaged in the dual-task condition. Moreover, the cognitive secondary task led to a higher speed variability. Our results demonstrate that there was not a significant relationship between the lane changes and the activity of listening to economic news. However, a meaningful difference was observed between general comprehension and deep comprehension on the one hand and driving performance on the other. Another aspect of our study concerning the drivers’ ages and their comprehension revealed a significant relationship between age above 75 and comprehension level. Drivers aging 75 and older showed a lower level of deep comprehension.ConclusionOur study demonstrates that elderly drivers compensated driving performance with safety margin adoption while they were cognitively engaged. In this condition, however, maintaining speed proved more demanding for drivers aging 75 and older.     

Analysis of driver roles: modelling the changing role of the driver in automated driving systems using EAST

ABSTRACT

The purpose of this paper is to analyse the role of the driver within automated driving systems using the Event Analysis of Systemic Teamwork (EAST) method. We already know that as the level of automation increases within the driving task, the role of the driver shifts from that of an active operator (i.e. a driver driving) to more of a passive monitor (i.e. a driver monitoring). Task, social and information networks were constructed using the Hierarchical Task Analysis of Driving and evidence from driver verbalisations collected during a previous study to further explore the changing role of the driver using network analysis. A ‘broken links’ approach was conducted to show that momentary engagement in non-driving-related secondary tasks within an automated driving system can dramatically change the structure of driving system.     

Flexible job-shop scheduling with flexible workdays,preemption, overlapping in operations and satisfaction criteria: an industrial application

This paper addresses a real scheduling problem, namely, a complex flexible job-shop scheduling problem (FJSP) with special characteristics (flexible workdays, preemption and overlapping in operations), where the objective is to maximise a satisfaction criterion defined through goal programming. To allow for flexible workdays, the solution representation of the classical FJSP is extended to consider overtime decisions and a sequence of time-cell states, which is used to model resource capability. A new temporal-constraint-handling method is proposed to solve the problem of overlapping in operations in a flexible-workday environment. Three solution methods are proposed to solve this scheduling problem: a heuristic method based on priority rules, a goal-guided tabu search (GGTS) and an extended genetic algorithm (EGA). In the GGTS, the neighbourhood functions are defined based on elimination approaches, and five possible neighbourhood functions (N0???N1???N2???N3???N4) are presented. The effectiveness and efficiency of the three solution methods are verified using dedicated benchmark instances. Computational simulations and comparisons indicate that the proposed N4-based GGTS demonstrates performance competitive with that of the EGA and the GGTSs based on the other neighbourhood functions (N0, N1, N2 and N3) for solving the scheduling problem.     

Evaluation of experience-based fatigue countermeasures

Background and objective

Strategies adopted by drivers in order to cope with fatigue and falling asleep at the wheel include a variety of activities that may invigorate the body and/or the mind. The objectives of the current study were to examine the effectiveness of an energy drink and a non-traditional manual-dexterity/mastication activity as fatigue countermeasures.

Method

Twenty subjects participated in this driving simulator study. Each driving session lasted 2 h and each driver drove under three conditions: after consumption of an energy drink, while engaged in a self-paced manual-dexterity/mastication secondary task (shelling and eating sunflower seeds), and in a control condition with neither. Fatigue effects were assessed on three dimensions: subjective evaluations (using the Swedish Occupational Fatigue Inventory), a physiological indicator (heart rate variability), and driving performance measures (speed, steering, and lane deviations).

Results

The subjective and physiological measures showed a significant effect of both treatments in counteracting the effects of fatigue when compared to the control condition. The results of the driving performance measures indicated that the energy drink was effective in counteracting fatigue, while the secondary task was as effective as the energy drink in counteracting fatigue on measures that did not rely on hand movements.

Conclusions

Drinking an energy drink prior to the driving task has a significant, positive effect in counteracting fatigue, though it may have long-term negative rebound effects. The manual-dexterity/mastication secondary task can temporarily counteract the subjective and physiological effects of fatigue while driving, but can interfere with vehicle handling.     

The effects of texting on driving performance in a driving simulator: The influence of driver age

Distracted driving is a significant contributor to motor vehicle accidents and fatalities, and texting is a particularly significant form of driver distraction that continues to be on the rise. The present study examined the influence of driver age (18–59 years old) and other factors on the disruptive effects of texting on simulated driving behavior. While ‘driving’ the simulator, subjects were engaged in a series of brief text conversations with a member of the research team. The primary dependent variable was the occurrence of Lane Excursions (defined as any time the center of the vehicle moved outside the directed driving lane, e.g., into the lane for oncoming traffic or onto the shoulder of the road), measured as (1) the percent of subjects that exhibited Lane Excursions, (2) the number of Lane Excursions occurring and (3) the percent of the texting time in Lane Excursions. Multiple Regression analyses were used to assess the influence of several factors on driving performance while texting, including text task duration, texting skill level (subject-reported), texting history (#texts/week), driver gender and driver age. Lane Excursions were not observed in the absence of texting, but 66% of subjects overall exhibited Lane Excursions while texting. Multiple Regression analysis for all subjects (N = 50) revealed that text task duration was significantly correlated with the number of Lane Excursions, and texting skill level and driver age were significantly correlated with the percent of subjects exhibiting Lane Excursions. Driver gender was not significantly correlated with Lane Excursions during texting. Multiple Regression analysis of only highly skilled texters (N = 27) revealed that driver age was significantly correlated with the number of Lane Excursions, the percent of subjects exhibiting Lane Excursions and the percent of texting time in Lane Excursions. In contrast, Multiple Regression analysis of those drivers who self-identified as not highly skilled texters (N = 23) revealed that text task duration was significantly correlated with the number of Lane Excursions. The present studies confirm past reports that texting impairs driving simulator performance. Moreover, the present study demonstrates that for highly skilled texters, the effects of texting on driving are actually worse for older drivers. Given the increasing frequency of texting while driving within virtually all age groups, these data suggest that ‘no texting while driving’ education and public service messages need to be continued, and they should be expanded to target older drivers as well.     

Perceptual and attentional effects on drivers' speed selection at curves

This paper describes an experiment comparing the relative effectiveness of various types of warnings on drivers' speed selection at curves. The experiment compared three types of curve warnings across three different curve types in a driving simulator. All of the warnings worked reasonably well for severe curves (45 km/h), regardless of demands from a secondary (cell phone) task. For less demanding curves, only those warnings with a strong perceptual component (i.e., implicit cues) were effective in reducing drivers' curve speeds in the presence of the cell phone task. The design implications of these data appear straightforward; curve warnings that contain perceptual components or emphasise the physical features of the curve work best, particularly in cognitively demanding situations. The cell phone task added to driver workload and drivers became less responsive to primary task demands (i.e., speeds were elevated and reaction times were longer).     

EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study

Driver distraction is responsible for a substantial number of traffic accidents. This paper describes the impact of an auditory secondary task on drivers’ mental states during a primary driving task. N = 20 participants performed the test procedure in a car following task with repeated forced braking on a non-public test track. Performance measures (provoked reaction time to brake lights) and brain activity (EEG alpha spindles) were analyzed to describe distracted drivers. Further, a classification approach was used to investigate whether alpha spindles can predict drivers’ mental states.     

Predictors of injury among younger and older adults in fatal motor vehicle crashes

With 2003 Fatality Analysis Reporting System data, we examined relationships among predictors of motor vehicle injury/fatality outcomes for younger (35–54 years) and older (65 years and older) drivers. Using the Precede-Proceed Model of Health Promotion as an organizing framework, we classified variables into person, vehicle and environment domains and conducted a multinomial logistic regression.Significant risk factors for older driver injuries were impact crashes at 1–3 o’clock (OR = 1.65; CI: 1.05–2.59), 7–9 o’clock angles (OR = 2.59; CI = 1.45–4.63), and driving with one passenger (OR = 2.25; CI: 1.58–3.20). Previous other motor vehicle convictions were significantly associated with reduced risk of injury (OR = 0.55; CI = 0.34–0.90). The 7–9 o’clock angle (OR = 3.06; CI: 1.83–5.12), and driving in daylight hours were risk factors for fatality among older drivers.Many risk factors (e.g., female gender, non-seatbelt use, rollover crashes, and vehicle body type), and protective factors (e.g., number of lanes and non-airbag deployment) were relevant for younger and older drivers. Findings showed relevant factors for drivers from both age groups, with some pointing to older adults, and set the stage for further research to develop injury and fatality prevention programs.     

Bayesian Inference: Parameter Estimation and Decisions

Xu has cataloged 165 minimum aberration (MA) regular fractional factorial (FF) designs with 2-levels and large run sizes N=128 (m=8–64 factors), N=256 (m=9–28, 109–119), N=512 (m=10–25, 238–246), N=1024 (m=11–24, 488–501), N=2048 (m=12–23), and N=4096 (m=13–24). Such an extensive catalog was produced because of an improved algorithm. We extend the catalog by 36 MA, 2-level regular FF designs: N=256 (m=29–36, 100–108), N=512 (m=26–29), N=1024 (m=25–28), N=2048 (m=24–32), and N=4096 (m=25–26). Although such enumeration problems are notoriously difficult with increased N and/or m, we brought the newly solved problems within computational reach by changing the vital isomorphism check component of Xu’s algorithm. Here we present a new, compact graph for solving regular design isomorphism problems and use the nauty program to solve the corresponding graph isomorphism problems. Supplemental appendices are available online.     

SOLUTION OF LARGE LINEAR SYSTEMS ON PIPELINED SIMD MACHINES

We developed a direct out-of-core solver for dense non-symmetric linear systems of ‘arbitrary’ size N×N. The algorithm fully employs the Basic Linear Algebra Subprograms (BLAS), and can therefore easily be adapted to different computer architectures by using the corresponding optimized routines. We used blocked versions of left-looking and right-looking variants of LU decomposition to perform most of the operations in Level 3 BLAS, to reduce the number of I/O operations and to minimize the CPU time usage. The storage requirements of the algorithm are only 2N×NB data elements where NB≪N. Depending on the sustained floating point performance and the sustained I/O rate of the given hardware, we derived formulas that allow for choosing optimal values of NB to balance between CPU time and I/O time. We tested the algorithm by means of linear systems derived from 3D-BEM for strongly and weakly singular integral equations and from interpolation problems for scattered data on closed surfaces in ℝ³. It took only about 2⋅5 CPU minutes on a 5 GFLOPS vector computer SNI S600/20 to solve a linear system of size 10000, which corresponds to a performance of 4⋅3 GFLOPS; a value of NB=650 gives a reasonable I/O time and the necessary main storage size is about 13 Mwords. In addition, we compared the algorithm with (1) an out-of-core version of GMRES and (2) a wavelet transform followed by in-core GMRES after thresholding. At least for boundary integral equations of classical boundary value problems of potential theory, the out-of-core version of GMRES is superior to the direct out-of-core solver and the wavelet transform since the algorithm converged after at most 5 iteration steps. It took about 17 s to solve a system with 8192 unknowns compared with 146 s for direct out-of-core and 402 s for wavelet transform followed by in-core GMRES. © 1997 by John Wiley & Sons, Ltd.     

Does attention capacity moderate the effect of driver distraction in older drivers?

With age, a decline in attention capacity may occur and this may impact driving performance especially while distracted. Although the effect of distraction on driving performance of older drivers has been investigated, the moderating effect of attention capacity on driving performance during distraction has not been investigated yet. Therefore, the aim was to investigate whether attention capacity has a moderating effect on older drivers’ driving performance during visual distraction (experiment 1) and cognitive distraction (experiment 2). In a fixed-based driving simulator, older drivers completed a driving task without and with visual distraction (experiment 1, N = 17, mean age 78 years) or cognitive distraction (experiment 2, N = 35, mean age 76 years). Several specific driving measures of varying complexity (i.e., speed, lane keeping, following distance, braking behavior, and crashes) were investigated. In addition to these objective driving measures, subjective measures of workload and driving performance were also included. In experiment 1, crash occurrence increased with visual distraction and was negatively related to attention capacity. In experiment 2, complete stops at stop signs decreased, initiation of braking at pedestrian crossings was later, and crash occurrence increased with cognitive distraction. Interestingly, for a measure of lane keeping (i.e., standard deviation of lateral lane position (SDLP)), effects of both types of distraction were moderated by attention capacity. Despite the decrease of driving performance with distraction, participants estimated their driving performance during distraction as good. These results imply that attention capacity is important for driving. Driver assessment and training programs might therefore focus on attention capacity. Nonetheless, it is crucial to eliminate driver distraction as much as possible given the deterioration of performance on several driving measures in those with low and high attention capacity.     

Personality trait theory and multitasking performance: implications for ergonomic design

Although system designers usually minimise the role of individual differences in operation, personality variables could explain differences in multitasking performance. A concomitant theoretical issue is whether primary or surface personality traits do a better job of predicting performance than the Five-Factor Model (FFM) or global traits. A sample of 174 undergraduates completed the Sixteen Personality Factor Questionnaire (16PF), which was followed by a performance task. A computer-based task that measured simultaneous performance on an arithmetic task and a mental rotation task was used to measure multitasking performance; scores measured the percent accuracy. Primary traits for low emotional sensitivity and high abstractedness, self-control, and general reasoning were all correlated with performance (R ² = .11), but global or traits corresponding to the FFM were not, except in one sporadic task trial. There was also a strong gender effect on performance. Implications for the study of personality traits in ergonomics are discussed.     

Video-based road commentary training improves hazard perception of young drivers in a dual task

This study used a video-based hazard perception dual task to compare the hazard perception skills of young drivers with middle aged, more experienced drivers and to determine if these skills can be improved with video-based road commentary training. The primary task required the participants to detect and verbally identify immediate hazard on video-based traffic scenarios while concurrently performing a secondary tracking task, simulating the steering of real driving. The results showed that the young drivers perceived fewer immediate hazards (mean = 75.2%, n = 24, 19 females) than the more experienced drivers (mean = 87.5%, n = 8, all females), and had longer hazard perception times, but performed better in the secondary tracking task. After the road commentary training, the mean percentage of hazards detected and identified by the young drivers improved to the level of the experienced drivers and was significantly higher than that of an age and driving experience matched control group. The results will be discussed in the context of psychological theories of hazard perception and in relation to road commentary as an evidence-based training intervention that seems to improve many aspects of unsafe driving behaviour in young drivers.     

Approximate Inverse Mapping in ECT, Based on Aperture Shifting and Neural Network Regression

The inversion procedure presented in this paper is based on the statistical regression of the inverse map between the spaces of ECT scan data, and of crack parameters. The mapping is realized by a combination between a statistical data processing step, i.e., a principal component transformation of the scan data, and an incremental resolution neural network training. Starting from the necessities of improving the detrimental conditioning of the regression and of providing the inversion approach with enhanced potential for automation, a novel shifting aperture mapping concept and a data fusion technique are proposed. Supplementing the primary mapping algorithm with these latter processing steps allows one to avoid the usual anomalous-region focusing approach and improves the inversion capabilities by allowing a dynamic reconstruction of the object's profile. Unconnected and multiply connected crack shapes are well estimated, that so far eluded most other inversion methods. For this primary validation of the completed algorithm, only synthetic B-scan data are used, which are collected by an optimized, high performance sensor on the interior of a metal tube.     

Multi-objective short-term production scheduling for open-pit mines: a hierarchical decomposition-based algorithm

This article presents a novel algorithm for solving a short-term open-pit production-scheduling problem in which several objectives, of varying priority, characterize the quality of each solution. A popular approach employs receding horizon control, dividing the horizon into N period-aggregates of increasing size (number of periods or span). An N-period mixed integer program (MIP) is solved for each period in the original horizon to incrementally construct a production schedule one period at a time. This article presents a new algorithm that, in contrast, decomposes the horizon into N period-aggregates of equal size. Given a schedule for these N periods, obtained by solving an N-period MIP, the first of these aggregates is itself decomposed into an N-period scheduling problem with guidance provided on what regions of the mine should be extracted. The performance of this hierarchical decomposition-based approach is compared with that of receding horizon control on a suite of data sets generated from an operating mine producing millions of tons of ore annually. As the number of objectives being optimized increases, the hierarchical decomposition-based algorithm outperforms receding horizon control, in a majority of instances.     

A time—memory trade-off frontwidth reduction algorithm for finite element analysis

A frontwidth reduction algorithm is presented with an execution time which may be traded against its primary memory requirement, making it possible to optimize the performance of the algorithm on a particular computer. With an amount of primary memory O(E^1/2), where E is the number of elements, the execution time of the algorithm is O(E^3/2), in two or three dimensions. The algorithm has two parts: first, new node-based data structures are derived from the conventional element list, then these structures are used to reorder the elements for reduced frontwidth.     

Cooperation between drivers and automation: implications for safety

The purpose of this paper is to develop a recently proposed framework of human–machine cooperation (Hoc, J.M., 2001. Towards a cognitive approach to human–machine cooperation in dynamic situations. International Journal of Human–Computer Studies, 54, 509–540) and apply it to the domain of in-car automation. Previous models of automation (e.g. Sheridan, T.B. and Verplanck, W.L., 1978. Human and computer control of undersea teleoperators. Cambridge, MA: MIT Man–Machine Systems Laboratory) delineate the roles of human and machine in a task-based manner and primarily from the viewpoint of machine requirements. However, with increasing arguments that automation should support the human operator rather than replace them (e.g. Young, M.S., Stanton, N.A., and Harris, D., 2007. Driving automation: learning from aviation about design philosophies. International Journal of Vehicle Design, 45(3), 323–338), Hoc's (2001) framework offers a means of modelling the interaction from the perspective of teamwork–that is, from the viewpoint of human requirements. In the present context, the framework describes how both the driver and the automation can be considered as separate agents pursuing their own goals while trying to facilitate a common task, but who may interfere with each other positively (e.g. mutual control) or negatively (mutual conflict). Vehicle automation, as an area of fervent research in ergonomics at present, provides the opportunity to explore the framework and use it to interpret current and emerging research findings. It is suggested here that many of the psychological problems underlying the introduction of in-car automation are a result of suboptimal communications between human and machine, and the framework is used to propose directions for future research in this area.