Automotive User Interfaces and Interactive Vehicular Applications

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AutomotiveUI 2011 Third International Conference on Automotive User Interfaces and Interactive Vehicular Applications November 29—December 2, 2011 ICT&S Center, University of Salzburg Salzburg, Austria ADJUNCT PROCEEDINGS WORKSHOP “COGNITIVE LOAD AND IN-VEHICLE HUMAN-MACHINE INTERACTION” Workshop Organizers: Andrew L. Kun University of New Hampshire Peter A. Heeman Oregon Health & Science University Tim Paek Microsoft Research W. Thomas Miller, III University of New Hampshire Paul A. Green Transportation Research Institute, University of Michigan Ivan Tashev Microsoft Research Peter Froehlich Telecommunications Research Center (FTW) Bryan Reimer AgeLab, MIT Shamsi Iqbal Microsoft Research Dagmar Kern Bertrandt Ingenieurbüro GmbH
The effects of visual-manual tasks on driving performance using a military vehicle on a dirt road Kerstan Cole Sandia National Laboratories PO Box 5800, MS 0830 Albuquerque, NM 87185 011-505-284-3012 kscole@sandia.gov ABSTRACT Driver distraction is deadly. In the United States alone, approximately 25 percent of all automobile accidents and 21 percent of all fatal accidents occur as a result of driver distraction in civilian settings. 1 However, it is more difficult to estimate the impact of driver distraction on military personnel. One similarity between civilian and military operating environments is the increasing proliferation of in-vehicle information systems. The purpose of this study was to examine the effects of three different visual-manual secondary tasks on participants’ driving performance. All tasks required participants to interact with touch screen technology. The results indicated that participants’ performance was significantly impaired during conditions when they executed a secondary task. Categories and Subject Descriptors H.1.2 [User/Machine Systems]: Human factors, human information processing General Terms Measurement, Performance, Experiments, Human Factors Keywords Distraction, driving, touch screen technology, military 1. INTRODUCTION Traffic related fatalities are one of the leading causes of death in both civilian and military settings. In the United States, 32,261 civilians died and another 2.3 million were injured in motor vehicle accidents in 2008. Among military personnel, motor vehicle crashes are the leading cause of death and are ranked in the top five leading causes of hospitalization among warfighters.[7] Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright held by authors AutomotiveUI’11, November 29-December 2, 2011 Salzburg, Austria Adjunct Proceedings J. Dan Morrow Sandia National Laboratories PO Box 5800, MS 1188 Albuquerque, NM 87185 011-505-284-9982 jdmorr@sandia.gov James Davis Army Research Laboratory HRED Army Proving Ground, MD 21005 011-410-278-5851 james.a.davis531.civ@mail.mil In fact, the United States Army reported that approximately 40 percent of the total number of noncombat deaths was caused by combat vehicle or motor vehicle accidents since the beginning of the War In Iraq in 2003.[10] Indeed, root cause analyses of these accidents indicated that their leading cause was “human error”. In the driving domain, human error may result from a variety of factors which include but are not limited to failures in perception or comprehension of environmental cues and the failure to project those environmental cues into the near future.[3] One precursor of the aforementioned factors is distraction. In fact, distracted driving accounts for an estimated 25 percent of all automobile accidents and 21 percent of all motor vehicle fatalities in the United States.[1] Moreover, numerous experimental and epidemiological studies indicate that distractions negatively affect drivers’ performance in civilian settings.[2,14] However, it is more difficult to estimate the impact of driver distraction in military settings for several reasons: Firstly, military motor vehicle crash rates are not widely published. Of the studies that examine crash rates, few distinguish crashes that involve personally owned vehicles (POV) from those that involve military motor vehicles (MMV). This means that frequently, reported crash rates are based on a combination of the two types of vehicles, and thus, may have very different characteristics. Similarly, the causes of military accidents are not widely published. Although “human error” has been cited as a contributing factor, the nature and impact of this factor on military crash statistics is relatively unknown.[4] Military operational driving environments differ from civilian driving environments. Military drivers operate under risky conditions, in both on and off-road contexts, in convoys, and in combat settings while wearing heavy gear, which may limit drivers’ physical mobility, hearing, and vision.[7] Thus, performance measures and interventions suitable for drivers operating POVs in civilian settings may not be applicable to drivers operating military owned vehicles in combat settings. In addition, laboratory studies that simulate military environments may not capture essential characteristics of both MMVs and their operating environments. Thus, performance observations resulting from a simulated environment may not translate to the actual operational context, thereby affecting external validity. Although differences between civilian and military driving environments exist, their in-vehicle context does share one similarity: in-vehicle informational systems (IVIS). These include but are not limited to global positioning systems (GPS), music applications, wireless communications technology, gaming
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Contents Preface 1 Welcome Note ...
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SpeeT - A Multimodal Interaction St
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Preface - 1 -
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Welcome Note from the Poster/Demo C
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Part I. Poster Abstracts - 5 -
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The use of in vehicle data recorder
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Information Extraction from the Wor
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IVAT (In-Vehicle Assistive Technolo
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ENGIN (Exploring Next Generation IN
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The HMISL language for modeling HMI
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Designing a Spatial Haptic Cue-base
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Investigating the Usage of Multifun
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Tobias Islinger Regensburg Universi
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Gas Station Creative Probing: The F
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A New Icon Selection Test for the A
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The Process of Creating an IVAT Plu
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Personas for On-Board Diagnostics -
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On Detecting Distracted Driving Usi
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ABSTRACT Natural and Intuitive Hand
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ABSTRACT 3D Theremin: A Novel Appro
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Open Car User Experience Lab: A Pra
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Situation-adaptive driver assistanc
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Determination of Mobility Context u
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Addressing Road Rage: The Effect of
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ABSTRACT New Apps, New Challenges:
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The Ethnographic (U)Turn: Local Exp
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(C)archeology -- Car Turns Outs & A
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An Approach to User-Focused Develop
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Part II. Interactive Demos - 53 -
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ABSTRACT The ROADSAFE Toolkit: Rapi
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SpeeT: A Multimodal Interaction Sty
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Dialogs Are Dead, Long Live Dialogs
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Page 89 and 90: Motivation • The mobility context
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Page 97 and 98: Figure 1. All Music and filtered vi
Page 99 and 100: Using Controller Widgets in 3D-GUI
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Page 107 and 108: Skinning as a method for differenti
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Page 131 and 132: 3. SYSTEM EVALUATION We conducted a
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Page 143 and 144: cognitive workload. The lateral pos
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Page 163: REMOTE TACTILE FEEDBACK The spatial
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AutomotiveUI 2011 Third Internation
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The selection of any petrol station
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visibility of available information
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Position Paper - Integrating Mobile
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Gartner, Smartphone sells have grow
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Unmuting Smart Phones in Cars: gett
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addition of new smart devices and s
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