What determines the take-over time? An integrated model approach of driver take-over after automated driving.

In recent years the automation level of driver assistance systems has increased continuously. One of the major challenges for highly automated driving is to ensure a safe driver take-over of the vehicle guidance. This must be ensured especially when the driver is engaged in non-driving related secondary tasks. For this purpose it is essential to find indicators of the driver's readiness to take over and to gain more knowledge about the take-over process in general. A simulator study was conducted to explore how drivers' allocation of visual attention during highly automated driving influences a take-over action in response to an emergency situation. Therefore we recorded drivers' gaze behavior during automated driving while simultaneously engaging in a visually demanding secondary task, and measured their reaction times in a take-over situation. According to their gaze behavior the drivers were categorized into "high", "medium" and "low-risk". The gaze parameters were found to be suitable for predicting the readiness to take-over the vehicle, in such a way that high-risk drivers reacted late and more often inappropriately in the take-over situation. However, there was no difference among the driver groups in the time required by the drivers to establish motor readiness to intervene after the take-over request. An integrated model approach of driver behavior in emergency take-over situations during automated driving is presented. It is argued that primarily cognitive and not motor processes determine the take-over time. Given this, insights can be derived for further research and the development of automated systems.

[1]  Johnell Brooks,et al.  Effects of remote and in-person verbal interactions on verbalization rates and attention to dynamic spatial scenes. , 2004, Accident Analysis and Prevention.

[2]  Klaus Bengler,et al.  Ubernahmezeiten beim hochautomatisierten Autofahren , 2012 .

[3]  Dario D. Salvucci,et al.  Threaded cognition: an integrated theory of concurrent multitasking. , 2008, Psychological review.

[4]  Natasha Merat,et al.  Transition to manual: driver behaviour when resuming control from a highly automated vehicle , 2014 .

[5]  Brian P. Bailey,et al.  Leveraging characteristics of task structure to predict the cost of interruption , 2006, CHI.

[6]  Raja Parasuraman,et al.  Performance Consequences of Automation-Induced 'Complacency' , 1993 .

[7]  David B. Kaber,et al.  The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task , 2004 .

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

[9]  Natasha Merat,et al.  Highly Automated Driving, Secondary Task Performance, and Driver State , 2012, Hum. Factors.

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

[11]  Mica R. Endsley,et al.  The Out-of-the-Loop Performance Problem and Level of Control in Automation , 1995, Hum. Factors.

[12]  Dean P. Chiang,et al.  Braking Response Times for 100 Drivers in the Avoidance of an Unexpected Obstacle as Measured in a Driving Simulator , 1996 .

[13]  Catherine Neubauer,et al.  Fatigue and Voluntary Utilization of Automation in Simulated Driving , 2012, Hum. Factors.

[14]  Klaus Bengler,et al.  Partially Automated Driving as a Fallback Level of High Automation , 2013 .

[15]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[16]  Dick de Waard,et al.  Behavioural impacts of Advanced Driver Assistance Systems - an overview. , 2001 .

[17]  David Crundall,et al.  Effects of experience and processing demands on visual information acquisition in drivers , 1998 .

[18]  Eyal M. Reingold,et al.  Time course of phonological activation during reading: Evidence from eye fixations. , 1995 .

[19]  Niels Taatgen,et al.  Toward a unified theory of the multitasking continuum: from concurrent performance to task switching, interruption, and resumption , 2009, CHI.

[20]  Stephen Monsell,et al.  Task-set reconfiguration with predictable and unpredictable task switches , 2003, Memory & cognition.

[21]  Klaus Bengler,et al.  “Take over!” How long does it take to get the driver back into the loop? , 2013 .

[22]  T H Rockwell,et al.  Eye movement analysis of visual information acquisition in driving: an overview , 1972 .

[23]  Linda Ng Boyle,et al.  Factors Affecting Glance Behavior when Interacting with In-Vehicle Devices: Implications from a Simulator Study , 2017 .

[24]  Jeff Allen Greenberg,et al.  EVALUATION OF DRIVER DISTRACTION USING AN EVENT DETECTION PARADIGM , 2003 .

[25]  David Shinar,et al.  Effects of practice, age, and task demands, on interference from a phone task while driving. , 2005, Accident; analysis and prevention.

[26]  Klaus Bengler,et al.  How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving , 2014 .

[27]  Neville A. Stanton,et al.  From fly-by-wire to drive-by-wire: Safety implications of automation in vehicles , 1996 .

[28]  P. Chapman,et al.  Visual Search of Driving Situations: Danger and Experience , 1998, Perception.

[29]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[30]  M R Stevenson,et al.  The impact of driver distraction on road safety: results from a representative survey in two Australian states , 2006, Injury Prevention.

[31]  Dot Hs The Impact of Driver Inattention On Near-Crash/Crash Risk: , 2006 .

[32]  Guy H. Walker,et al.  AUTOMATING THE DRIVER'S CONTROL TASKS , 2001 .

[33]  Catherine Neubauer,et al.  The Effects of Cell Phone Use and Automation on Driver Performance and Subjective State in Simulated Driving , 2012 .

[34]  Michael Weber,et al.  Autonomous driving: investigating the feasibility of car-driver handover assistance , 2015, AutomotiveUI.

[35]  S. Monsell Task switching , 2003, Trends in Cognitive Sciences.

[36]  Natasha Merat,et al.  Control Task Substitution in Semiautomated Driving , 2012, Hum. Factors.

[37]  M. Lesch,et al.  Driving performance during concurrent cell-phone use: are drivers aware of their performance decrements? , 2004, Accident; analysis and prevention.

[38]  Paul Green VISUAL AND TASK DEMANDS OF DRIVER INFORMATION SYSTEMS , 1999 .

[39]  N L Schweitzer A FIELD STUDY ON BRAKING RESPONSES DURING DRIVING (II). , 1995 .

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

[41]  J. Gregory Trafton,et al.  Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal , 2003, Int. J. Hum. Comput. Stud..

[42]  Johanna K. Kaakinen,et al.  Task effects on eye movements during reading. , 2010, Journal of experimental psychology. Learning, memory, and cognition.

[43]  Frank Flemisch,et al.  Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations , 2012, Cognition, Technology & Work.

[44]  Alexandra Neukum,et al.  The effect of urgency of take-over requests during highly automated driving under distraction conditions , 2014 .

[45]  Jean Underwood,et al.  Visual attention while driving: sequences of eye fixations made by experienced and novice drivers , 2003, Ergonomics.

[46]  W J Horrey,et al.  Dissociation between driving performance and drivers' subjective estimates of performance and workload in dual-task conditions. , 2009, Journal of safety research.

[47]  Natasha Merat,et al.  Surrogate in-vehicle information systems and driver behaviour: effects of visual and cognitive load in simulated rural driving , 2005 .

[48]  Charles A. Green,et al.  Human Factors Issues Associated with Limited Ability Autonomous Driving Systems: Drivers’ Allocation of Visual Attention to the Forward Roadway , 2017 .

[49]  Annika F.L. Larsson,et al.  Learning from experience: familiarity with ACC and responding to a cut-in situation in automated driving , 2014 .

[50]  Jeff Allen Greenberg,et al.  Driver Distraction: Evaluation with Event Detection Paradigm , 2003 .

[51]  H Alm,et al.  Changes in driver behaviour as a function of handsfree mobile phones--a simulator study. , 1994, Accident; analysis and prevention.

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

[53]  David B. Kaber,et al.  Situation awareness and workload in driving while using adaptive cruise control and a cell phone , 2005 .

[54]  David Crundall,et al.  Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers' eye movements in day, night and rain driving. , 2010, Accident; analysis and prevention.

[55]  Neville A. Stanton,et al.  Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence , 2014 .

[56]  Maria Rita Ciceri,et al.  How does a collision warning system shape driver's brake response time? The influence of expectancy and automation complacency on real-life emergency braking. , 2015, Accident; analysis and prevention.

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

[58]  M. Schmitter-Edgecombe,et al.  Costs of a predictable switch between simple cognitive tasks following severe closed-head injury. , 2006, Neuropsychology.

[59]  Klaus Bengler,et al.  Taking Over Control from Highly Automated Vehicles , 2014 .

[60]  Mikael B. Skov,et al.  Interacting with in-vehicle systems: understanding, measuring, and evaluating attention , 2009 .

[61]  Nadja Schömig,et al.  The Interaction Between Highly Automated Driving and the Development of Drowsiness , 2015 .

[62]  G Underwood,et al.  Visual attention and the transition from novice to advanced driver , 2007, Ergonomics.

[63]  Markus Maurer,et al.  Rechtsfolgen zunehmender Fahrzeugautomatisierung , 2012 .

[64]  Niels Taatgen,et al.  Toward a Unified View of Cognitive Control , 2011, Top. Cogn. Sci..

[65]  Johan Engström,et al.  Effects of visual and cognitive load in real and simulated motorway driving , 2005 .

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

[67]  Natasha Merat,et al.  Engaging with Highly Automated Driving: To be or Not to be in the Loop? , 2017 .

[68]  Kathrin Zeeb,et al.  Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving. , 2016, Accident; analysis and prevention.

[69]  M R Endsley,et al.  Level of automation effects on performance, situation awareness and workload in a dynamic control task. , 1999, Ergonomics.

[70]  Katja Kircher,et al.  Driver distraction : a review of the literature , 2007 .

[71]  Mark S. Young,et al.  Malleable Attentional Resources Theory: A New Explanation for the Effects of Mental Underload on Performance , 2002, Hum. Factors.

[72]  Jayesh Patel,et al.  Factors influencing subjective ranking of driver distractions. , 2008, Accident; analysis and prevention.

[73]  Mark S. Young,et al.  Attention and automation: New perspectives on mental underload and performance , 2002 .

[74]  Renwick E. Curry,et al.  Flight-deck automation: promises and problems , 1980 .