Manual Driver Distraction: Theory, Effects, and Mitigation

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In addition, not all false alarms are created equal. False alarms that drivers associate with events in the environment lead them to trust the system and thus become more likely to comply with subsequent alerts. False alarms that appear as if they occur randomly tend to have the opposite effect Lees and Lee, Adapting a threshold for alerts based on the degree of driver distraction could reduce false alarms by raising the threshold for attentive drivers.

This approach could lead to an interesting paradox in that the drivers who most need alerts are also the most likely to consider them false alerts. For example, a distracted driver might not notice a hazard even with the alert and so might not appreciate the value of the alert. Providing a driver with information on roadway demands and hazards after a drive, similar to the post-drive feedback for distraction, could help him or her understand the reason for the alerts.

More generally, drivers are more likely to benefit from vehicle technology that augments driver attention by informing through continuous information rather than alerting through discrete warnings. Recent studies suggest the potential benefits of post-drive feedback McGehee et al. In one study, teenage drivers drove with a camera that captured abrupt braking and steering responses.

Driver Distraction: Theory, Effects, and Mitigation - CRC Press Book

The resulting video and a summary of events was shared with their parents weekly, leading to an 89 percent reduction in the number of events triggered by risky drivers compared to the baseline period. Even after the feedback was removed, the rate of events remained low until the end of the study six weeks later. Whether feedback would be accepted or effective in helping experienced drivers manage distracting technology remains to be seen. Conclusion Technology changes the nature of driving by introducing new vulnerabilities and capacities Woods and Dekker, Infotainment systems introduce new distractions that can undermine safety.

Driver-assistance technologies promise to mitigate these distractions and improve safety. But we will not reap the potential benefits of these devices with a technology-only approach.

Driver distraction theory effects and mitigation ebook

Drivers tend to reject or misuse imperfect technologies that automate driving rather than augmenting driver capabilities. Cognitive engineering methods can show the way to using technology to leverage human capabilities to improve the safety and performance of complex systems by enhancing self-awareness and the awareness of potentially distracting technology. Increasingly pervasive and powerful driving technologies, as in other domains, can blur the boundaries between the human and the technological, posing practical, theoretical, and philosophical issues about safety and performance, which increasingly depend on a complex interaction of driver, in-vehicle technology, and the driving situation Lees and Lee, References Bensinger, K.

Chrysler will offer wireless Internet access in models. Los Angeles Times. Donmez, B. Boyle, and J. The impact of driver distraction mitigation strategies on driving performance. Human Factors 48 4 : — Boyle, J. Lee, and G. Safety implications of providing real-time feedback to distracted drivers. Accident Analysis and Prevention, 39 3 : — Erlhagen, W. Dynamic field theory of movement preparation. Psychological Review 3 : — Evans, L. Traffic Safety.

Bloomfield Hills, Mich. Hoffman, J. A dynamic field theory model of shared visual attention while driving. Hollnagel, E. Woods, and N. Resilience Engineering: Concepts and Precepts. Burlington, Vt. Klauer, S. Dingus, V. Neale, J. Sudweeks, and D. DOT HS Washington, D.

Lee, J. Human factors and ergonomics in automation design. Hoboken, N. Hoffman, and E. Collision warning design to mitigate driver distraction. McGehee, T.

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Brown, and M. Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Human Factors 44 2 : — Regan, and K. Distraction as a breakdown of multi-level control at different time horizons.

Driver Distraction: Theory, Effects, and Mitigation

Regan, J. Boca Raton, Fla. Lees, M. The influence of distraction and driving context on driver response to imperfect collision warning systems. Ergonomics 50 8 : — Enhancing Information Acquisition from the Driving Environment. Liang, Y. Reyes, and J. Real-time detection of driver cognitive distraction using support vector machines.

Driver Distraction

In press. Non-intrusive detection of driver cognitive distraction in real-time using Bayesian networks. Transportation Research Board. McGehee, D. Raby, C. Carney, J. Lee, and M. Extending parental mentoring using an event-triggered video intervention in rural teen drivers. Journal of Safety Research 38 2 : — Michon, J.

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Explanatory pitfalls and rule-based driver models. Accident Analysis and Prevention 21 4 : — Moray, N. Designing for Attention. Baddeley and L. Parasuraman, R. Humans and automation: use, misuse, disuse, abuse. Human Factors 39 2 : — This article investigates the nature of bus driver distraction at a major Algerian public transport company, including the sources of distraction present, and their effects on driver performance through the application of a novel framework of ergonomics methods.

The framework represents a novel approach for assessing distraction in a Real—world context. The findings suggest that there are a several sources of distraction that could potentially distract bus drivers while driving, including those that derive from the driving task itself and those that derive from the additional requirements associated with bus operation, such as passenger and ticketing—related distractions.

Taxonomy of the sources of bus driver distraction identified is presented, along with a discussion of proposed countermeasures designed to remove the sources identified or mitigate their effects on driver performance. This study through technique SHERPA is an analysis of bus operation indicated that some distraction—induced errors could potentially be made by drivers who are engaging in distracting activities while driving the bus.

These include critical safety errors, which have the potential to adversely affect bus driver performance and bus crash risk, and operational errors, which have the potential to reduce the efficiency of bus driver performance when undertaking bus operation tasks other than driving the bus. Keywords: ergonomics applications, task analysis, bus driver distraction, operation tasks, conclusions.

Distraction is a complex, multifaceted phenomenon and, despite the immense research effort devoted to studying the concept over the past two decades, there is still much to understand about its mechanisms and its relationship with other aspects of human cognition and behaviour. There exists a large and expanding body of research that has documented the myriad ways in which distraction can impact on driving performance and safety. These include reduced longitudinal 3 , 4 and lateral control; 5 reduced situation awareness; 6 , 7 and impaired response times to roadway hazards.

According to these taxonomies, if a distracted driver were unable to stop at a red traffic signal, their failure to see the traffic signal following the distraction would not be captured; rather, the distraction itself would be listed as the error. Other taxonomies list distraction as a casual factor in driver errors, but do not indicate the mechanisms by which it contributes. As part of research undertaken for a major transport company in Algeria, the potential for bus drivers to be distracted while driving buses was investigated.

Specifically, the research aimed to identify what sources of distraction bus drivers are exposed to while operating buses, what their potential impact on performance and safety is likely to be, and what can be done to minimize driver exposure to them. The aim of this paper is to present the findings derived from this research and to outline the methodology applied. Driver distraction is acknowledged internationally as a significant road safety concern. It also contains the types of distraction visual, cognitive, physical associated with each distraction source and information regarding the odds of being involved in a crash or near—crash associated with each source and the percentage of distraction—related crashes in which the source has been found to be a contributing factor.

Researchers typically distinguish between a range of different types of distraction, namely visual, cognitive and physical manual distraction; all of which have been shown to have disparate effects on driving performance. In addition to conventional driving, 29 some research has focussed on distraction in the commercial transport sector, such as heavy goods vehicle 30 and truck driving. The ergonomics methods have been proposed for identifying sources of distraction and for assessing its effects on driver performance see 35 for a critical review.

For example, methods used previously for identifying sources of driver distraction include reviewing police crash databases, 36 using self—report questionnaires 37 and conducting naturalistic driving studies involving in—vehicle video recording. For example, naturalistic driving studies are difficult and expensive to set up and run, self—report data are flawed for many reasons, 38 and bus crash databases containing the level of detail required are rarely available. Aside from presenting the findings derived from our case study on bus driver distraction, this paper presents a novel approach for assessing driver distraction in the real world where in—vehicle recording, simulation, and laboratory tests are not available.

The methodology applied presented in Figure 1 involved the application of various ergonomics methods in an integrated manner. Using ergonomics methods in an integrated manner is attractive several reasons, and has been used or recommended to study a range of ergonomics constructs, including workload, 40 human error, 41 situation awareness 42 and command and control.

The data obtained was analyzed using content analysis procedures to identify sources and effects of distraction and informed the development of a Hierarchical Task Analysis HTA 44 of bus operation. HTA is used to describe systems in terms of the goals, sub—goals and physical and cognitive operations required to achieve them, including the goal—based human—machine interactions required during task performance.

The contextual triggers of goals and operations are also described. The utility of HTA is also such that its outputs inform the conduct of further analyses, including human error identification, 45 , 46 which was used in this case to assess the effects of distraction on bus driver performance. A video camera was used to record bus operation activities during the observational study component, and observational transcripts were constructed on—line using pen and paper. The observational studies were undertaken naturalistically during standard bus operation, with the observers located in the passenger area of the bus near to the bus drivers.

A content analysis was performed on the data collected and the HTA to identify potential sources of distraction. To ensure validity, all analysis outputs were reviewed and refined where appropriate by an SME from the transport company. An extract of the HTA of bus operation is presented in Figure 1. Overall, seven key goal—based categories of tasks that the bus drivers currently perform while operating buses were identified. The taxonomy contains all potential sources of distraction that were identified during the study. The potential sources of distraction identified were categorized into seven main categories Figure 2.

Of the various HEI methods available, SHERPA is the most popular and has the most supporting validation evidence, with recent application in aviation, 47 healthcare 46 and the military. Each the behaviour classification has a set of associated errors. For each credible error identified, a description of the form that the error would take is provided and the analyst describes any consequences associated with the error and any error recovery steps that could be taken. The final step involves specifying any potential design remedies i.

This involved taking each bottom level task step from the HTA and predicting the driver errors that might arise in the event of the driver being distracted, either physically, visually or cognitively, while performing the task in question. The findings suggest that bus driver distraction is a potentially significant road safety problem within the public transport sector. The risk of distraction is particularly significant in this domain where drivers are compelled, as part of their job, to perform additional secondary tasks over and above the primary task of driving the vehicle.

These additional tasks are coupled with the fact that driving a bus is, by itself, a high workload task. Several sources of distraction that could potentially distract bus drivers while driving buses were identified.

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These include those that are present during conventional driving, such as eating, drinking and roadside advertisements, but also include an additional set of distraction sources that are present due to the requirements associated with bus operation, such as those deriving from interaction with passengers and ticketing machines.

These distraction sources were classified into the following categories:. However, before informed, appropriate countermeasures can be developed and implemented, it is clear that much further targeted research is required. The current study represents an initial exploratory study in this area, and while the findings are useful, further investigation is required. Beyond identifying sources of bus driver distraction, a contribution of this study has been the development of a novel framework of ergonomics methods that can be used to examine, in a real—world context, driver distraction in the public transport domain.

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Methods integration has been cited as a useful approach for studying a range of ergonomics constructs, and is particularly useful when multiple perspectives e. In this case the method was useful since it allowed analysts to describe the bus operation system through HTA in a manner that supported the identification of sources of distraction HTA, supported by interviews, focus groups, documentation review, observation and some of the anticipated effects of different distracters on driver performance through SHERPA. As noted by, 52 it is important to develop analytical tools that allow researchers to make a priori judgments about the likely effects of different sources of distraction on driving performance, given the considerable time and expense associated with more traditional impact assessment techniques.

SHERPA is a promising tool for this purpose, and makes the important conceptual link between distraction and human error. Recent thinking and discussion about the mechanisms that appear to mediate the effects of distraction on driving performance 52 make it possible to further improve and validate the use of SHERPA as an analytical tool for the assessment of distraction.

The approach presented here is a useful alternative when existing crash data is inadequate and there is little opportunity to undertake simulated or test track assessments of distraction. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and build upon your work non-commercially.

Withdrawal Policies Publication Ethics. Research Article Volume 4 Issue 2. Driver distraction Driver distraction is acknowledged internationally as a significant road safety concern. Ergonomics methods for identifying bus driver distraction The ergonomics methods have been proposed for identifying sources of distraction and for assessing its effects on driver performance see 35 for a critical review. Materials and procedure A video camera was used to record bus operation activities during the observational study component, and observational transcripts were constructed on—line using pen and paper.

Figure 2 Sources of bus driver distraction identified. These distraction sources were classified into the following categories: Technology—related distractions; operational distractions; Passenger—related distractions; environmental distractions; bus cabin—related distractions; infrastructure—related distractions; and personal distractions. Driver distraction and driver inattention: definition, relationship and taxonomy.

Accid Anal Prev. Examining the relationship between driver distraction and driving errors: a discussion of theory, studies and methods. Safety Science. Effects of naturalistic cell phone conversations on driving performance. Journal of Safety Research.