Considering visual-manual tasks performed during highway driving in the context of two different sets of guidelines for embedded in-vehicle electronic systems

Abstract The Alliance of Automobile Manufacturers and the National Highway Traffic Safety Administration have each developed a set of guidelines intended to help developers of embedded in-vehicle systems minimize the visual demand placed on a driver interacting with the visual-manual interface of the system. Though based on similar precepts, the guidelines differ in the evaluation methodologies and the criteria used to define safe levels of visual demand. The current study compared the pass/fail conclusions from applying the two guidelines. Four visual-manual tasks were evaluated using two embedded in-vehicle systems (Volvo Sensus, Chevrolet MyLink) during highway driving. Only a preset radio tuning task met the threshold for acceptable visual demand in both guidelines. The pass/fail conclusions for three of the four tasks [manual radio tuning (fail), preset radio tuning (pass), easy contact calling (fail)] performed using either system were the same for both guidelines; calling a contact with multiple possible numbers using MyLink failed both guidelines, and with Sensus the task passed the Alliance guidelines but not NHTSA’s. Exploratory analyses suggested that broadening the age range of the participant sample specified in the Alliance guidelines beyond 45–65 year olds did not change pass/fail conclusions. Results from a Monte Carlo simulation suggested that relying on data from a single trial per the NHTSA guidelines may reduce the repeatability of pass/fail conclusions. Interestingly, the manual radio tuning task failed to pass both sets of guidelines, even though the organizations used it as a reference task for setting acceptable levels of visual demand. Perhaps this indicates that radios have become more difficult to tune than the ones that provided the basis for the guidelines; however, naturalistic driving studies have not indicated increased risk from tuning more modern radios. Analysis of glance behavior during naturalistic driving may provide opportunities to further refine the acceptable thresholds for visual demand.

[1]  Omer Tsimhoni,et al.  Visual Demand of Driving and the Execution of Display-Intensive in-Vehicle Tasks , 2001 .

[2]  W W Wierwille,et al.  AUTOMOTIVE ERGONOMICS. CHAPTER 14. VISUAL AND MANUAL DEMANDS OF IN- CAR CONTROLS AND DISPLAYS , 1993 .

[3]  Johan Engström,et al.  Sensitivity of eye-movement measures to in-vehicle task difficulty , 2005 .

[4]  Richard A Young Need for Revised Total Eyes-Off-Road Criterion in the NHTSA Distraction Guidelines: Track Radio-Tuning Data , 2017 .

[5]  Bryan Reimer,et al.  The validity of driving simulation for assessing differences between in-vehicle informational interfaces: A comparison with field testing , 2010, Ergonomics.

[6]  Marco Dozza,et al.  Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk , 2014 .

[7]  Heikki Summala,et al.  Aging and Time-Sharing in Highway Driving , 2005, Optometry and vision science : official publication of the American Academy of Optometry.

[8]  David G. Kidd,et al.  Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems , 2015, Ergonomics.

[9]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[10]  John W. Senders,et al.  THE ATTENTIONAL DEMAND OF AUTOMOBILE DRIVING , 1967 .

[11]  Justin M. Owens,et al.  Radio Tuning Effects on Visual and Driving Performance Measures: Simulator and Test Track Studies , 2013 .

[12]  Yulan Liang,et al.  A Looming Crisis , 2014 .

[13]  SAVE-IT SAfety VEhicles using adaptive Interface Technology ( Task 2 a ) Estimating Driving Task Demand from Crash Probabilities : A Review of the Literature and Assessment of Crash Databases , 2004 .

[14]  Tuomo Kujala,et al.  Testing environment and verification procedure for in-car tasks with dynamic self-paced driving scenarios , 2015 .

[15]  John Martin,et al.  Distraction Effects of Manual Number and Text Entry While Driving - Part 1 , 2013 .

[16]  Andrew W. Gellatly,et al.  Visual Attention Demand Evaluation of Conventional and Multifunction in-Vehicle Information Systems , 2000 .

[17]  Gary Burnett,et al.  Assessment of the occlusion technique as a means for evaluating the distraction potential of driver support systems , 2006 .

[18]  Jeremy D Sudweeks,et al.  An Analysis of Driver Inattention Using a Case-Crossover Approach On 100-Car Data: Final Report , 2010 .

[19]  Christopher A. Monk,et al.  R We Fooling Ourselves: Does the Occlusion Technique Shortchange R Estimates? , 2017 .

[20]  Omer Tsimhoni,et al.  Address Entry While Driving: Speech Recognition Versus a Touch-Screen Keyboard , 2004, Hum. Factors.

[21]  Annie Rydström,et al.  Better ways to calculate pass/fail criteria for the eye glance measurement using driving simulator test , 2015 .

[22]  John D. Lee,et al.  How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving , 2012, Hum. Factors.

[23]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[24]  Andrew W. Gellatly,et al.  Visual Sampling of In-Vehicle Text Messages: Effects of Number of Lines, Page Presentation, and Message Control , 2005 .

[25]  Bryan Reimer,et al.  The Effects of a Production Level "Voice-Command" Interface on Driver Behavior: Summary Findings on Reported Workload, Physiology, Visual Attention, and Driving Performance , 2013 .

[26]  Cory Siebe,et al.  Distracted Driving and Risk of Road Crashes among Novice and Experienced Drivers , 2014 .

[27]  Tuomo Kujala,et al.  The Attentional Demand of Automobile Driving Revisited , 2016, Hum. Factors.

[28]  Klaus Bengler,et al.  Evaluation of in-vehicle HMI using occlusion techniques: experimental results and practical implications. , 2004, Applied ergonomics.