However, in driving simulator studies participants scan a non-uniformly illuminated visual scene. If unaccounted for, this non-uniformity in illumination might introduce an error in our estimate of the TEPR. Oskar Palinko and I will have a paper at ETRA 2012 [2] extending our previous work [3], in which we established that it is possible to separate the pupil’s light reflex from the TEPR. While in our previous work TEPR was the result of participants’ engagement in an aural task, in our latest experiment TEPR is due to engagement in a visual task.

The two experiments taken together support our main hypothesis that it is possible to disambiguate (and not just separate) the two effects even in complicated environments, such as a driving simulator. We are currently designing further experiments to test this hypothesis.

References

[1] Jackson Beatty, “Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources,” Psychological Bulletin, 276-292, 91(2)

[2] Oskar Palinko, Andrew L. Kun, “Exploring the Effects of Visual Cognitive Load and Illumination on Pupil Diameter in Driving Simulators,” to appear at ETRA 2012

[3] Oskar Palinko, Andrew L. Kun, “Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies,” Driving Assessment 2011

Why have this workshop? Interactions with in-vehicle electronic devices can interfere with the primary task of driving. The concept of cognitive load helps us understand the extent to which these interactions interfere with the driving task and how this interference can be mitigated. While research results on in-vehicle cognitive load are frequently presented at automotive research conferences and in related journals, so far no dedicated forum is available for focused discussions on this topic. This workshop aims to fill that void.

Submissions to the workshop are due October 17. Topics of interest include, but are not limited to:

- Cognitive load estimation in the laboratory,
- Cognitive load estimation on the road,
- Sensing technologies for cognitive load estimation,
- Algorithms for cognitive load estimation,
- Performance measures of cognitive load,
- Physiological measures of cognitive load,
- Visual measures of cognitive load,
- Subjective measures of cognitive load,
- Methods for benchmarking cognitive load,
- Cognitive load of driving,
- Cognitive overload and cognitive underload,
- Approaches to cognitive load management inspired by human-human interactions.

For a detailed description of workshop goals take a look at the call for papers.

So what is next for PNDs? In a driving simulator study to be presented at MobileHCI 2011 [1], Zeljko Medenica, Tim Paek, Oskar Palinko and I explored two ideas:

• Augmented reality PND: An augmented reality PND overlays route guidance on the real world using a head-up display. Our version is simulated and we simply project the route guidance on the simulator screens along with the driving simulation images. Augmented reality PNDs are not yet available commercially for cars.
• Street-view PND: This PND uses a simplified version of augmented reality. It overlays route guidance on a sequence of still images of the road. The images and overlay are displayed on a head-down display. Google Maps Navigation runs on smart phones and can be used with street view.

The following video demonstrates the two PNDs.

Our findings indicate that augmented reality PNDs allow for excellent visual attention to the road ahead and excellent driving performance. In contrast, street-view PNDs can have a detrimental effect on both. Thus, while further research is clearly needed, it might be best if navigation with a street view PND was handled by a passenger and not by the driver.

References

[1] Zeljko Medenica, Andrew L. Kun, Tim Paek, Oskar Palinko, “Augmented Reality vs. Street Views: A Driving Simulator Study Comparing Two Emerging Navigation Aids,” to appear at MobileHCI 2011

Last week Oskar Palinko gave a talk at Driving Assessment 2011 introducing our work on disambiguating the effects of cognitive load and light on pupil diameter in driving simulator studies [2]. We hypothesized that we can simply subtract the effect of lighting on pupil diameter from the combined effect of light and cognitive load and produce an estimate of cognitive load only. We tested the hypothesis through an experiment in which participants were given three tasks:

• Cognitive task with varying cognitive load and constant lighting. This task was adapted from the work of Klingner et al. [3]. Participants listened to a voice counting from 1 to 18 repeatedly. Participants were told that every sixth number (6, 12, and 18) might be out of order and were instructed to push a button if they detected an out-of-order number. This task induced increased cognitive load at every sixth number as participants focused on the counting sequence. A new number was read every 1.5 seconds, thus cognitive load (and pupil diameter) increased every 6 x 1.5 sec = 9 seconds.
• Visual task with constant cognitive load (assuming no daydreaming!) and varying lighting. Participants were instructed to follow a visual target which switched location between a white, a gray and a black truck. The light reaching the participant’s eye varied as the participant’s gaze moved from one truck to another. Participants held their gaze on a truck for 9 seconds, allowing the pupil diameter ample time to settle.
• Combined task with varying cognitive load and lighting. Participants completed the cognitive and visual tasks in parallel. We synchronized the cognitive and visual tasks such that increases in cognitive load occurred after the pupil diameter stabilized in response to moving the gaze between trucks. Synchronization was straightforward as the cognitive task was periodic with 9 seconds and in the visual task lighting intensity also changed every 9 seconds.

Our results confirm that, at least in this simple case, our hypothesis holds and we can indeed detect changes in cognitive load under varying lighting conditions. We are planning to extend this work by introducing scenarios in which participants drive in realistic simulated environments. Under such scenarios gaze angles, and thus the amount of light reaching participants’ eyes, will change rapidly, making the disambiguation more complex, and of course more useful.

References

[1] Jackson Beatty, “Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources,” Psychological Bulletin, 276-292, 91(2)

[2] Oskar Palinko, Andrew L. Kun, “Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies,” Driving Assessment 2011

[3] Jeff Klingner, Rashit Kumar, Pat Hanrahan, “Measuring the Task-Evoked Pupillary Response with a Remote Eye Tracker,” ETRA 2008

Zeljko’s PhD committee includes Paul Green (UMTRI), Tim Paek (Microsoft Research), Nicholas Kirsch (UNH) and Tom Miller (UNH). Thanks to all for serving!

References

[1] Andrew L. Kun, Tim Paek, Zeljko Medenica, Nemanja Memarovic, Oskar Palinko, “Glancing at Personal Navigation Devices Can Affect Driving: Experimental Results and Design Implications,” Automotive UI 2009

[2] Zeljko Medenica, Andrew L. Kun, Tim Paek, Oskar Palinko, “Augmented Reality vs. Street Views: A Driving Simulator Study Comparing Two Emerging Navigation Aids,” to appear at MobileHCI 2011

My presentation focused on pervasive (or ubiquitous) computing for law enforcement. I encouraged participants to ask the following question:

“What should be the focus of R&D efforts targeting percom technologies for emergency responders?”

CVTA President Scott McCormick (in picture below) and WisDOT’s John Corbin led the meeting superbly – thanks to both for including me in this effort.

For more pictures from the event visit Flickr.

Bryan spent time in the Project54 lab discussing various aspects of driving simulator and field studies. He then gave a thought-provoking talk reviewing results from multiple studies exploring driver workload and distraction. I expecially enjoyed his discussion of physiological measures that can be used to estimate workload. E.g. Bryan has found that heart rate is a robust estimate of workload and is often more useful than the often-used measure of heart rate variability. Bryan also discussed work on validating driving simulator results through field studies. His data indicate that driving simulator results can be used to predict relative changes in workload measures under different situations in real-life driving. However, the actual values of the measures collected in simulator and field studies often differ significantly.

For more pictures visit Flickr.

The Project54 lab was created in 1999 in partnership with the New Hampshire Department of Safety to improve technology for New Hampshire law enforcement. Project54’s in-car system integrates electronic devices in police cruisers into a single voice-activated system. Project54 also integrates cruisers into agency-wide communication networks. The Project54 system has been deployed in over 1000 vehicles in New Hampshire in over 180 state and local law enforcement agencies.

Research focus

Both the PhD and the MS student will focus on the relationship between various in-car user interface characteristics and the cognitive load of interacting with these interfaces, with the goal of designing interfaces that do not significantly increase driver workload. Work will involve developing techniques to estimate cognitive load using performance measures (such as the variance of lane position), physiological measures (such as changes in pupil diameter) and subjective measures (such as the NASA-TLX questionnaire).

The work will utilize experiments in Project54’s world-class driving simulator laboratory which is equipped with two research driving simulators, three eye trackers and a physiological data logger. Laboratory experiments will be complemented by field deployments in law enforcement agencies such as the New Hampshire State Police, which operates over 300 police cruisers. Project54 has deployed a state-wide data update infrastructure for the New Hampshire State Police which allows remote updates to in-car experimental software and remote collection of experimental data.

 Appointment

The PhD student will be appointed for four years, and the MS student for two years. Initial appointments will be for one year, starting between June and September 2011. Continuation of funding will be dependent on satisfactory performance. Appointments will be a combination of research and teaching assistantships. Compensation will include tuition, fees, health insurance and academic year and summer stipend.

How to apply

For application instructions, and for general information, email Andrew Kun, Project54 Principal Investigator at andrew.kun@unh.edu. Please attach a current CV.

On November 11 and 12 I was at the AutomotiveUI 2010 conference serving as program co-chair with Susanne Boll. The conference was hosted by Anind Dey at CMU and co-chaired by Albrecht Schmidt.

The conference was successful and really fun. I could go on about all the great papers and posters (including two posters from our group at UNH [1,2]) but in this post I’ll only mention two: John Krumm’s keynote talk and, selfishly, my own talk (this is my blog after all). John gave an overview of his work with data from GPS sensors. He discussed work on prediction of where people will go, his experiences with location privacy and with creating road maps. Given that John is, according to his own website, the “all seeing, all knowing, master of time, space, and dimension,” this was indeed a very informative talk OK in all seriousness, the talk was excellent. I find John’s work on prediction of people’s destination and selected route the most interesting. One really interesting effect of having accurate predictions, and people sharing such data in the cloud, would be on routing algorithms hosted in the cloud. If such an algorithm could know where all of us are going at any instant of time, it could propose routes that overall allow efficient use of roads, reduced pollution, etc.

My talk focused on collaborative work with Alex Shyrokov and Peter Heeman on multi-threaded dialogues. Specifically, I talked about designing spoken tasks for human-human dialogue experiments for Alex’s PhD work [3]. Alex wanted to observe how pairs of subjects switch between two dialogue threads, while one of the subjects is also engaged in operating a simulated vehicle. Our hypothesis is that observed human-human dialogue behaviors can be used as the starting point for designing computer dialogue behaviors for in-car spoken dialogue systems. One of the suggestions we put forth in the paper is that the tasks for human-human experiments should be engaging. These are the types of tasks that will result in interesting dialogue behaviors and can thus teach us something about how humans manage multi-threaded dialogues.

Next year the conference moves back to Europe. The host will be Manfred Tscheligi in Salzburg, Austria. Judging by the number of submissions this year and the quality of the conference, we can look forward to many interesting papers next year, both from industry and from academia. Also, the location will be excellent – just think Mozart, Sound of Music (see what Rick Steves has to say), and world-renowned Christmas markets!

References

[1] Zeljko Medenica, Andrew L. Kun, Tim Paek, Oskar Palinko, “Comparing Augmented Reality and Street View Navigation,” AutomotiveUI 2010 Adjunct Proceedings

[2] Oskar Palinko, Sahil Goyal, Andrew L. Kun, “A Pilot Study of the Influence of Illumination and Cognitive Load on Pupil Diameter in a Driving Simulator,” AutomotiveUI 2010 Adjunct Proceedings

[3] Andrew L. Kun, Alexander Shyrokov, Peter A. Heeman, “Spoken Tasks for Human-Human Experiments: Towards In-Car Speech User Interfaces for Multi-Threaded Dialogue,” AutomotiveUI 2010

One of Zig’s associates to take the stage was Larry Heck, Chief Scientist, Speech at Microsoft. Larry believes that there are three areas of research and development that will combine to make speech a part of everyday interactions with computers. First, the advent of ubiquitous computing and the need for natural user interfaces (NUIs) means that we cannot keep relying on GUIs and keyboards for many of our computing needs. Second, cloud computing makes it possible to gather rich data to train speech systems. Finally, with advances in speech technology we can expect to see search move beyond typing keywords (which is what we do today sitting at our PCs) to conversational queries (which is what people are starting to do on mobile phones).

I attended four other talks with topics relevant to my research. Brigitte Richardson discussed her work on Ford’s Sync. It’s exciting to hear that Ford is coming out with an SDK that will allow integrating devices with Sync. This appears to be a similar approach to ours at Project54 – we also provide an SDK which can be used to write software for the Project54 system [1]. Eduardo Olvera of Nuance discussed the differences and similarities between designing interfaces for speech interaction and those for interaction on a small form factor screen. Karen Kaushansky of TellMe discussed similar issues focusing on customer care. Finally, Kathy Lee, also of TellMe, discussed her work on a diary study exploring when people are willing to talk to their phones. This work reminded me of an experiment in which Ronkainen et al. asked participants to rate the social acceptability of mobile phone usage scenarios they viewed in video clips [2].

I also had a chance to give a talk reviewing some of the results of my collaboration with Tim Paek of Microsoft Research. Specifically, I discussed the effects of speech recognition accuracy and PTT button usage on driving performance [3] and the use of voice-only instructions for personal navigation devices [4]. The talk was very well received by the audience of over 25, with many follow-up questions. Tim also gave this talk earlier this year at Mobile Voice 2010.

For pictures from SpeechTEK 2010 visit my Flickr page.

References

[1] Andrew L. Kun, W. Thomas Miller, III, Albert Pelhe and Richard L. Lynch, “A software architecture supporting in-car speech interaction,” IEEE Intelligent Vehicles Symposium 2004

[2] Sami Ronkainen, Jonna Häkkilä, Saana Kaleva, Ashley Colley, Jukka Linjama, “Tap Input as an Embedded Interaction Method for Mobile Devices,” TEI 2007

[3] Andrew L. Kun, Tim Paek, Zeljko Medenica, “The Effect of Speech Interface Accuracy on Driving Performance,” Interspeech 2007

[4] Andrew L. Kun, Tim Paek, Zeljko Medenica, Nemanja Memarovic, Oskar Palinko, “Glancing at Personal Navigation Devices Can Affect Driving: Experimental Results and Design Implications,” Automotive UI 2009