Shannon C. Roberts

Scrolling and Driving: How an MP3 Player and Its Aftermarket Controller Affect Driving Performance and Visual Behavior

Objective: The aim of this study was to assess how scrolling through playlists on an MP3 player or its aftermarket controller affects driving performance and to examine how drivers adapt device use to driving demands. Background: Drivers use increasingly complex infotainment devices that can undermine driving performance. The goal activation hypothesis suggests that drivers might fail to compensate for these demands, particularly with long tasks and large search set sizes. Method: A total of 50 participants searched for songs in playlists of varying lengths using either an MP3 player or an aftermarket controller while negotiating road segments with traffic and construction in a medium-fidelity driving simulator. Results: Searching through long playlists (580 songs) resulted in poor driving performance and required more long glances (longer than 2 s) to the device compared with other playlist lengths. The aftermarket controller also led to more long glances compared with the MP3 player. Drivers did not adequately adapt their behavior to roadway demand, as evident in their degraded driving performance. No significant performance differences were found between short playlists, the radio-tuning task, and the no-task condition. Conclusion: Selecting songs from long playlists undermined driving performance, and drivers did not sufficiently adapt their use of the device to the roadway demands, consistent with the goal activation hypothesis. The aftermarket controller degraded rather than enhanced performance. Application: Infotainment systems should support drivers in managing distraction. Aftermarket controllers can have the unintended effect of making devices carried into the car less compatible with driving. These results can motivate development of new interfaces as alternatives to scrolling lists.

City browser: developing a conversational automotive HMI

This paper introduces City Browser, a prototype multimodal, conversational, spoken language interface for automotive navigational aid and information access. A study designed to evaluate the impact of age and gender on device interaction errors, perceptions and experiences with the system along with physiological indices of workload is outlined. Preliminary results, plans for further analysis and a larger scale user evaluation are presented.

Measurement of driver calibration and the impact of feedback on drivers’ estimates of performance

Recent studies focused on driver calibration show that drivers are often miscalibrated, either over confident or under confident, and the magnitude of this miscalibration changes under different conditions. Previous work has demonstrated behavioral and performance benefits of feedback, yet these studies have not explicitly examined the issue of calibration. The objective of this study was to examine driver calibration, i.e., the degree to which drivers are accurately aware of their performance, and determine whether feedback alters driver calibration. Twenty-four drivers completed a series of driving tasks (pace clocks, traffic light, speed maintenance, and traffic cones) on a test track. Drivers drove three different blocks around the test track: (1) baseline block, where no participants received feedback; (2) feedback block, where half of the participants received performance feedback while the other half received no feedback; (3) a no feedback block, where no participants received feedback. Results indicated that across two different calibration measures, drivers were sufficiently calibrated to the pace clocks, traffic light, and traffic cone tasks. Drivers were not accurately aware of their performance regarding speed maintenance, though receiving feedback on this task improved calibration. Proper and accurate measurements of driver calibration are needed before designing performance feedback to improve calibration as these feedback systems may not always yield the intended results.

A Model-Based Approach to Predicting the Performance of Insider Threat Detection Systems

Recent high profile security breaches have highlighted the importance of insider threat detection systems for cybersecurity. However, issues such as high false positive rates and concerns over data privacy make it difficult to predict performance within an enterprise environment. These and other issues limit an organizations ability to effectively apply these tools. In this paper, we present an approach to predicting the performance of insider threat detection systems that leverages enterprise-level modeling. We provide a proof of concept of our modeling approach by applying it to a synthetic dataset and comparing its predictions to the ground truth. The results shown here to predict performance can enable enterprises to compare tools and ultimately allow them to make better informed decisions about which insider threat detection systems to deploy.

Deciphering 140 Characters Text Mining Tweets On #DriverDistraction

Objective: Conduct an exploratory analysis of driver distraction tweets using text mining. Background: Twitter is a popular social networking site with a wealth of data that is both explanatory and predictive of current trends and events. Data from Twitter may also prove useful in understanding the attitudes and opinions surrounding distracted driving. Method: Tweets posted between January 29, 2012 and April 12, 2013 containing the words ‘driver distraction’ or ‘driving distraction’ were collected. Text mining was used to extract patterns from the tweets in terms of timelines, frequencies, and associations. Results: Over 8,000 tweets were collected and contained information about users’ personal experience with driver distraction as well as various news articles about driver distraction. Conclusion: Twitter data provide a real-time snapshot of the attitudes surrounding of distracted driving. Application: Information from social media can complement traditional driving data sources, such as simulator studies, naturalistic studies, and epidemiological data, to create a more holistic picture of distracted driving.

Using Agent-Based Modeling to Predict the Diffusion of Safe Teenage Driving Behavior Through an Online Social Network

Objective: To use agent-based modeling to examine how peer influence in a social network affects behavior change among teenage drivers. Background: Teenagers are involved in more fatal driving crashes than any other age group. A solution to the problem is to utilize both feedback systems and online social networks to promote safe driving behavior. Methods: Using a combination of intention (from the Theory of Planned Behavior) and habit, agent-based modeling was used to predict the spread of safe driving behavior through an online social network after the implementation of a feedback system Results: The initial conditions of the social network (e.g., the number of agents to initially engage in safe driving behavior and the number of opinion leaders who use the feedback system), as well as the threshold used to determine intention drive safely had a significant effect on the final number of agents to engage in safe driving behavior. Conclusion: Agent-based modeling suggests that leveraging the power of feedback systems and social networks can lead to a positive change in teenage driving behavior. Application: Agent-based modeling can be a viable tool in predicting teenage driver behavior given the correct choice of parameters.

Does Order Matter? Investigating the Effect of Sequence on Glance Duration During On-Road Driving

Previous literature has shown that vehicle crash risks increases as drivers’ off-road glance duration increases. Many factors influence drivers’ glance duration such as individual differences, driving environment, or task characteristics. Theories and past studies suggest that glance duration increases as the task progresses, but the exact relationship between glance sequence and glance durations is not fully understood. The purpose of this study was to examine the effect of glance sequence on glance duration among drivers completing a visual-manual radio tuning task and an auditory-vocal based multi-modal navigation entry task. Eighty participants drove a vehicle on urban highways while completing radio tuning and navigation entry tasks. Forty participants drove under an experimental protocol that required three button presses followed by rotation of a tuning knob to complete the radio tuning task while the other forty participants completed the task with one less button press. Multiple statistical analyses were conducted to measure the effect of glance sequence on glance duration. Results showed that across both tasks and a variety of statistical tests, glance sequence had inconsistent effects on glance duration—the effects varied according to the number of glances, task type, and data set that was being evaluated. Results suggest that other aspects of the task as well as interface design effect glance duration and should be considered in the context of examining driver attention or lack thereof. All in all, interface design and task characteristics have a more influential impact on glance duration than glance sequence, suggesting that classical design considerations impacting driver attention, such as the size and location of buttons, remain fundamental in designing in-vehicle interfaces.