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The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages.

A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions.

Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving.

The results provide support to guide countermeasures and vehicle design.

Purpose – Driver distraction and other forms of driver inattention remain significant road safety problems. The purpose of this chapter is to explore recent developments in theoretical and empirical research on driver distraction and inattention and provide the reader with a sense for, and understanding of, the key issues.

Methodology – Key references from the literature are reviewed and discussed.

Findings – First, we discuss one way of conceptualising the distinction between driver distraction and other forms of inattention, as well as the mechanisms which may underlie these forms of inattention. Second, we underscores how driver distraction may derive from a plethora of sources, and how the potential for performance degradation deriving from driver interaction with these sources may be moderated by a range of factors. Third, we review recent literature on the types of impairments in driving performance and safety associated with driver distraction. Fourth, we outline recent literature on driver distraction and inattention in the realm of highly automated vehicles that will drive the transport future. Finally, we discuss some promising strategies aimed at preventing and mitigating the impact of driver distraction.

Research implications – There are many gaps in the driver distraction literature that need to be addressed. In addition, further research needs to be undertaken to examine the role of driver distraction in the realm of highly automated vehicles.

Practical implications – The findings point towards of a range of injury prevention countermeasures that have potential to prevent and mitigate driver distraction.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

Most US states exempt police officers from restrictive distracted laws, and most agencies require officers to use mobile data computers while driving. The purpose of this paper is to examine the impact of a text-based distraction task on officer driving performance.

Experienced police patrol officers (n=80) participated in controlled laboratory experiments during which they drove a high-fidelity driving simulator on four separate occasions; twice immediately following five consecutive 10:40 hour patrol shifts (fatigued condition) and again 72 hours after completing the last shift in a cycle (rested condition). In each condition, officers drove identical, counterbalanced 15-minute courses with and without distraction tasks. The research used a within- and between-subjects design.

A generalized linear mixed-model analysis of driving performance showed that officers’ distracted driving performance had significantly greater lane deviation (F=88.58, df=1,308, p < 0.001), instances of unintentionally leaving assigned driving lane (F=64.76, df=1,308, p < 0.001), and braking latency (F=200.82, df=1,308, p < 0.001) than during non-distracted drives. These measures are leading indicators for collision risk.

Simulated driving tasks presented were generally less challenging than patrol driving and likely underestimate the impact of distraction on police driving.

Police officers appear to drive significantly worse while distracted, and their routine experience with using text-based communication devices while driving does not mitigate the risks associated with doing so. Study results suggest that policing organizations should modify policies, practices, training, and technologies to reduce the impact of distraction on officers’ driving. Failing to do so exposes officers and the communities they serve to unnecessary hazards and legal liabilities.

Concentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.

The system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.

The accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.

The system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.