Linda Angell

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.

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

The importance of task duration and related measures in assessing the distraction potential of in-vehicle tasks

The issue of task duration in the assessment of driver distraction has been a controversial topic. In the development of J2364 Navigation and Route Guidance Function Accessibility While Driving, task duration and a related criterion were the most difficult parts of achieving consensus. The current discussion is restricted to a few key criticisms of task duration and duration-related measures of driving performance. We provide data-driven reasons why criticisms of duration-related measures, though important, are not sufficient to negate the value of these measures. Further, we point to naturalistic driving research that indicates it is glances away from the road scene prior to critical events that predominate in real-world crashes and near-misses. Rather than suggesting duration-related measures be abandoned, naturalistic driving research underscores the importance of using driver metrics like total eyes-off-road time as well as single glance durations. Finally, task length is an attribute of a task and HMI design, which can be modified through re-design and therefore will influence duration-related performance. We argue that duration is particularly important as a tool to assess where interventions to limit distraction might be applied.

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset

PROBLEM Distracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community. METHOD This project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event. RESULTS 1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset. DISCUSSION We anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving. PRACTICAL APPLICATIONS The coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the conduct of more complex research.

What Can Be Predicted from Six Seconds of Driver Glances

We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, its valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.

Pointing Towards Future Automotive HMIs: The Potential for Gesture Interaction

Gesture provides a new design space for in-vehicle human-machine interaction. It could potentially mitigate emerging conflicts between the increasing functionality of todays vehicles and the very limited space that is available for implementing these functions within the drivers reach. However, because gesture requires manual input, it may cause unintended consequences for drivers rather than supporting concurrent driving tasks as it is meant to do. This workshop will explore the potential of in-vehicle gesture interaction, as well as the cautions that must be exercised during its implementation. Participants will contribute to the discussion of design guidelines for gesture interaction; discussion of the advantages and disadvantages of gesture interaction based on properties of secondary tasks; will propose promising uses of gesture for in-vehicle interaction; and will advise systematic approaches for guiding the development of gesture interactions that will minimize the impact to or even facilitate primary driving tasks. The discussion within this workshop will also consider the different phases of automation as a design factor and discuss how to adapt gesture interactions to the changing demands in manual driving control.

Differentiating Cognitive Load Using a Modified Version of AttenD

Voice interfaces offer promise in allowing drivers to keep their eyes on-road and hands on-wheel. In relieving visualmanual demand, there is the potential for voice-enabled interfaces to inadvertently shift the burden of load to cognitive resources. Measurement approaches are needed that can identify when and to what extent cognitive load is present during driving. A modified form of the AttenD algorithm was applied to assess the amount of cognitive load present in a set of auditory-vocal task interactions. These tasks were subset from a larger on-road study conducted in the Boston area of driver response during use of an in-vehicle voice system [22]. The modified algorithm differentiated among the set of auditory-vocal tasks examined -- and may be useful to HMI practitioners who are working to develop and evaluate HMIs to support drivers in managing their attention to the road, and in the development of real-time driver attention monitoring systems.

Evaluating Demands Associated with the Use of Voice-Based In-Vehicle Interfaces

This panel addresses current efforts associated with the evaluation of demands associated with the use of voice-based in-vehicle interfaces. As generally implemented, these systems are perhaps best characterized as mixed-mode interfaces drawing upon varying levels of auditory, vocal, visual, manual and cognitive resources. Numerous efforts have quantified demands associated with these systems and several have proposing evaluation methods. However, there has been limited discussion in the scientific literature on the benefits and drawbacks of various measures of workload; appropriate reference points for comparison (i.e. just driving, visual-manual versions of the task one is looking to replace, etc.); the relationship of demand characteristics to safety; and practical design considerations that can be gleamed from efforts to date. Panelists will discuss scientific progress in the topic areas. Each panelist is expected to present a brief perspective followed by discussion and Q&A.

Assessment of Naturalistic Use Patterns of Advanced Infotainment Systems

Objective: The objective was to examine naturalistic usage of infotainment systems to assess use characteristics and patterns. Background: Infotainment systems continue to evolve in terms of their capabilities and information availability, raising concerns about their distraction potential. Assessing potential distraction requires understanding how challenging different tasks are and how frequently they occur during driving. Method: High-end infotainment system use was observed across 17 participants over a period of approximately 4 weeks each. One of two different infotainment systems was provided to participants. Audio, video, and driving performance data were collected and observed by trained reductionists. The two infotainment systems integrated iPod™, satellite radio, CD/DVD/MP3 playback, AM/FM, and, in one case, navigation functionalities. Systems differed in their vehicle integration and advanced infotainment features offered. Results: The median participant interacted with the infotainment systems once every 4 hr (90th percentile: 6.1 interactions/hr). More than 50% of these interactions involved adjusting the volume. Although there were a few lengthy interactions, the median duration was 2.2 s (90th percentile: 24.6 s), which required measurable visual involvement when compared to a matched baseline. The median total eyes-off-road time across interactions was 1 s (90th percentile: 11.4 s) and differed significantly across type of system interaction. Longer interactions tended to occur when the vehicle was stationary. Conclusion: Drivers habitually interact with infotainment systems while driving; this includes advanced functions. Some self-regulation was observed. Application: These data provide a comparison basis for use in examining driver interactions with future infotainment systems.

Linking the Detection Response Task and the AttenD Algorithm Through Assessment of Human–Machine Interface Workload

Multitasking related demands can adversely affect drivers’ allocation of attention to the roadway, resulting in delays or missed responses to roadway threats and to decrements in driving performance. Robust methods for obtaining evidence and data about demands on and decrements in the allocation of driver attention are needed as input for design, training, and policy. The detection response task (DRT) is a commonly used method (ISO 17488) for measuring the attentional effects of cognitive load. The AttenD algorithm is a method intended to measure driver distraction through real-time glance analysis, in which individual glances are converted into a scalar value using simple rules considering glance duration, frequency, and location. A relationship between the two tools is explored. A previous multitasking driving simulation study, which used the remote form of the DRT to differentiate the demands of a primary visual–manual human–machine interface from alternative primary auditory–vocal multimodal human–machine interfaces, was reanalyzed using AttenD, and the two analyses compared. Results support an association between DRT performance and AttenD algorithm output. Summary statistics produced from AttenD profiles differentiate between the demands of the human–machine interfaces considered with more power than analyses of DRT response time and miss rate. Among discussed implications is the possibility that AttenD taps some of the same attentional effects as the DRT. Future research paths, strategies for analyses of past and future data sets, and possible application for driver state detection are also discussed.