Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity.

Studies show high correlations between drivers' off-road glance duration or pattern and the frequency of crashes. Understanding drivers' use of peripheral vision to detect and react to threats is essential to modelling driver behavior and, eventually, preventing crashes caused by visual distraction. A between-group experiment with 83 participants was conducted in a high-fidelity driving simulator. Each driver in the experiment was exposed to an unexpected, critical, lead vehicle deceleration, when performing a self-paced, visual-manual, tracking task at different horizontal visual eccentricity angles (12°, 40° and 60°). The effect of visual eccentricity on threat detection, glance and brake response times was analyzed. Contrary to expectations, the driver glance response time was found to be independent of the eccentricity angle of the secondary task. However, the brake response time increased with increasing task eccentricity, when measured from the driver's gaze redirection to the forward roadway. High secondary task eccentricity was also associated with a low threat detection rate and drivers were predisposed to perform frequent on-road check glances while executing the task. These observations indicate that drivers use peripheral vision to collect evidence for braking during off-road glances. The insights will be used in extensions of existing driver models for virtual testing of critical longitudinal situations, to improve the representativeness of the simulation results.

[1]  Wim van Winsum,et al.  The Effects of Cognitive and Visual Workload on Peripheral Detection in the Detection Response Task , 2018, Hum. Factors.

[2]  Andrew Gelman,et al.  The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..

[3]  Marc Green,et al.  "How Long Does It Take to Stop?" Methodological Analysis of Driver Perception-Brake Times , 2000 .

[4]  Heikki Summala,et al.  Maintaining Lane Position with Peripheral Vision during In-Vehicle Tasks , 1996, Hum. Factors.

[5]  J. Findlay,et al.  Active Vision: The Psychology of Looking and Seeing , 2003 .

[6]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[7]  I. Rentschler,et al.  Peripheral vision and pattern recognition: a review. , 2011, Journal of vision.

[8]  Alberto Morando,et al.  Users’ Response to Critical Situations in Automated Driving: Rear-Ends, Sideswipes, and False Warnings , 2021, IEEE Transactions on Intelligent Transportation Systems.

[9]  M. Rajadhyaksha,et al.  Confocal imaging-guided laser ablation of basal cell carcinomas: an ex vivo study. , 2015, The Journal of investigative dermatology.

[10]  Michael Land,et al.  Which parts of the road guide steering? , 1995, Nature.

[11]  T Dukic,et al.  Effect of button location on driver's visual behaviour and safety perception , 2005, Ergonomics.

[12]  Cristina Olaverri-Monreal,et al.  In-vehicle displays: Driving information prioritization and visualization , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[13]  Marieke Hendrikje Martens,et al.  Measuring distraction: the Peripheral Detection Task , 2000 .

[14]  E. C. M. van de Weijgert.  Foveal load and peripheral task performance: tunnel vision or general interference , 1993 .

[15]  Michael E Rakauskas,et al.  Effects of naturalistic cell phone conversations on driving performance. , 2004, Journal of safety research.

[16]  John D. Lee,et al.  Driver Distraction : Theory, Effects, and Mitigation , 2008 .

[17]  Jan Theeuwes,et al.  VISUAL SEARCH OF TRAFFIC SCENES: ON THE EFFECT OF LOCATION EXPECTATIONS , 1993 .

[18]  S. McKee,et al.  The detection of motion in the peripheral visual field , 1984, Vision Research.

[19]  Gustav Markkula,et al.  How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models , 2019, Hum. Factors.

[20]  Marco Dozza,et al.  How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2 , 2015 .

[21]  Pushpa Choudhary,et al.  Mobile phone use during driving: Effects on speed and effectiveness of driver compensatory behaviour. , 2017, Accident; analysis and prevention.

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

[23]  Gary E. Riccio,et al.  Responses to Optical Looming in the Retinal Center and Periphery , 1990 .

[24]  Takayuki Kondoh,et al.  Direct Evidence of the Inverse of TTC Hypothesis for Driver’s Perception in Car-Closing Situations , 2014 .

[25]  Martin Fränzle,et al.  A comprehensive and harmonized method for assessing the effectiveness of advanced driver assistance systems by virtual simulation: The P.E.A.R.S , 2015 .

[26]  Harry L. Snyder.  Braking Movement Time and Accelerator-Brake Separation , 1976 .

[27]  S M Anstis,et al.  Letter: A chart demonstrating variations in acuity with retinal position. , 1974, Vision research.

[28]  Raymond J Kiefer,et al.  Developing an inverse time-to-collision crash alert timing approach based on drivers' last-second braking and steering judgments. , 2005, Accident; analysis and prevention.

[29]  M. Carrasco.  Visual attention: The past 25 years , 2011, Vision Research.

[30]  Walter W. Wierwille,et al.  An initial model of visual sampling of in-car displays and controls , 1993 .

[31]  François-Xavier Li,et al.  Dodging a Ball Approaching on a Collision Path: Effects of Eccentricity and Velocity , 2001 .

[32]  Bryan Reimer,et al.  Detection of brake lights while distracted: Separating peripheral vision from cognitive load , 2019, Attention, Perception, & Psychophysics.

[33]  Gustav Markkula,et al.  A quantitative driver model of pre-crash brake onset and control , 2017 .

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

[35]  Krister Wolff,et al.  A Review of Near-Collision Driver Behavior Models , 2012, Hum. Factors.

[36]  Michael A. Regan,et al.  Driver distraction: A review of the literature , 2003 .

[37]  Heikki Summala,et al.  Top-Down and Bottom-Up Processes in Driver Behavior at Roundabouts and Crossroads , 2000 .

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

[39]  M. Sommer,et al.  Corollary discharge circuits in the primate brain , 2008, Current Opinion in Neurobiology.

[40]  Simon F Giszter,et al.  Motor primitives—new data and future questions , 2015, Current Opinion in Neurobiology.

[41]  Richard M Wilkie,et al.  Does gaze influence steering around a bend? , 2008, Journal of vision.

[42]  William P. Berg,et al.  Attention-capturing properties of high frequency luminance flicker: Implications for brake light conspicuity , 2007 .

[43]  Heikki Summala,et al.  DETECTION THRESHOLDS IN CAR FOLLOWING SITUATIONS AND PERIPHERAL VISION: IMPLICATIONS FOR POSITIONING OF VISUALLY DEMANDING IN-CAR DISPLAYS , 1999 .

[44]  K A Brookhuis,et al.  A comparison of different ways to approximate time-to-line crossing (TLC) during car driving. , 2000, Accident; analysis and prevention.

[45]  B T Davies,et al.  Preliminary Investigation of Movement Time Between Brake and Accelerator Pedals in Automobiles , 1969, Human factors.

[46]  Harry Zhang,et al.  Identification of Real-Time Diagnostic Measures of Visual Distraction With an Automatic Eye-Tracking System , 2006, Hum. Factors.

[47]  M. Sivak.  The Information That Drivers Use: Is it Indeed 90% Visual? , 1996, Perception.

[48]  Marco Dozza,et al.  Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving , 2014 .

[49]  Erik Hollnagel,et al.  THE EMPEROR'S NEW CLOTHES Or Whatever Happened To "Human Error"? , 2001 .

[50]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[51]  T. Dingus,et al.  Distracted driving and risk of road crashes among novice and experienced drivers. , 2014, The New England journal of medicine.

[52]  Ernst Pöppel,et al.  Effects of display position of a visual in-vehicle task on simulated driving. , 2006, Applied ergonomics.

[53]  Leonard Evans,et al.  Traffic Safety and the Driver , 1991 .

[54]  C. Billington,et al.  Orexin activation counteracts decreases in nonexercise activity thermogenesis (NEAT) caused by high-fat diet , 2017, Physiology & Behavior.

[55]  M. A. Recarte,et al.  Mental workload while driving: effects on visual search, discrimination, and decision making. , 2003, Journal of experimental psychology. Applied.

[56]  Marco Dozza,et al.  A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance , 2019, IEEE Transactions on Intelligent Transportation Systems.

[57]  Trent Victor,et al.  Distraction Assessment Methods Based on Visual Behavior and Event Detection , 2009 .

[58]  P C Burns,et al.  PLACING VISUAL DISPLAYS IN VEHICLES: WHERE SHOULD THEY GO? , 2001 .

[59]  Christopher D. Wickens,et al.  In-Vehicle Glance Duration , 2007 .

[60]  Brett R Fajen,et al.  Calibration, information, and control strategies for braking to avoid a collision. , 2005, Journal of experimental psychology. Human perception and performance.

[61]  H Summala,et al.  Driving experience and perception of the lead car's braking when looking at in-car targets. , 1998, Accident; analysis and prevention.

[62]  Gustav Markkula,et al.  Modeling driver control behavior in both routine and near-accident driving , 2014 .

[63]  David N. Lee,et al.  A Theory of Visual Control of Braking Based on Information about Time-to-Collision , 1976, Perception.

[64]  Thomas A. Dingus,et al.  The 100-Car Naturalistic Driving Study Phase II – Results of the 100-Car Field Experiment , 2006 .

[65]  M. Land.  Eye movements and the control of actions in everyday life , 2006, Progress in Retinal and Eye Research.

[66]  J. Kruschke.  Rejecting or Accepting Parameter Values in Bayesian Estimation , 2018 .

[67]  Marco Dozza,et al.  A Bayesian Reference Model for Visual Time-Sharing Behaviour in Manual and Automated Naturalistic Driving , 2020, IEEE Transactions on Intelligent Transportation Systems.

[68]  Bryan Reimer,et al.  How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving. , 2018, Accident; analysis and prevention.

[69]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[70]  N Yoshitsugu,et al.  JAMA'S SAFETY GUIDELINE ON IN-VEHICLE DISPLAY SYSTEMS (THE STUDY OF MONITOR LOCATION OF IN-VEHICLE) , 2000 .

[71]  Wiebo H Brouwer,et al.  The effect of visual field defects on driving performance: a driving simulator study. , 2002, Archives of ophthalmology.

[72]  Johan Engström,et al.  Great expectations: a predictive processing account of automobile driving , 2018 .

[73]  Karen Holopigian,et al.  Multidimensional visual field maps: relationships among local psychophysical and local electrophysiological measures. , 2004, Journal of rehabilitation research and development.

[74]  Laurence B McCullough,et al.  Letter to the Editors , 2012, The American journal of bioethics : AJOB.

[75]  Gustav Markkula,et al.  A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. , 2016, Accident; analysis and prevention.