Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

This paper assessed four types of human–machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286–297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19–36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition—low), sphere (information acquisition—high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers’ initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

[1]  Mark S. Young,et al.  Drive-by-wire: The case of driver workload and reclaiming control with adaptive cruise control , 1997 .

[2]  Nadja Schömig,et al.  The Importance of Interruption Management for Usefulness and Acceptance of Automated Driving , 2017, AutomotiveUI.

[3]  J C F de Winter,et al.  Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat. , 2017, Accident; analysis and prevention.

[4]  Neville A. Stanton,et al.  Detection of new in-path targets by drivers using Stop & Go Adaptive Cruise Control. , 2011, Applied ergonomics.

[5]  Donna Lloyd,et al.  In Touch with the Future: The Sense of Touch from Cognitive Neuroscience to Virtual Reality , 2014, PRESENCE: Teleoperators and Virtual Environments.

[6]  Neville A. Stanton,et al.  Driving Performance After Self-Regulated Control Transitions in Highly Automated Vehicles , 2017, Hum. Factors.

[7]  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 .

[8]  Klaus Bengler,et al.  Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop. , 2017, Applied ergonomics.

[9]  Peter A. Hancock,et al.  Fatigue and Automation-Induced Impairments in Simulated Driving Performance , 1998 .

[10]  Neville A. Stanton,et al.  When Communication Breaks Down or What was that? – The Importance of Communication for Successful Coordination in Complex Systems☆ , 2015 .

[11]  Boussaad Soualmi,et al.  Augmented reality versus classical HUD to take over from automated driving: An aid to smooth reactions and to anticipate maneuvers , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[12]  Erik Hollnagel,et al.  Joint Cognitive Systems: Foundations of Cognitive Systems Engineering , 2005 .

[13]  Lutz Lorenz,et al.  Designing take over scenarios for automated driving , 2014 .

[14]  Håkan Jansson,et al.  An analysis of driver’s steering behaviour during auditory or haptic warnings for the designing of lane departure warning system , 2003 .

[15]  Yusuke Nakamura,et al.  A genome-wide association study identifies two susceptibility loci for duodenal ulcer in the Japanese population , 2012, Nature Genetics.

[16]  Markus Zimmermann,et al.  Using gamification to motivate human cooperation in a lane-change scenario , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[17]  Klaus Bengler,et al.  Evaluation of a Contact Analog Head-Up Display for Highly Automated Driving , 2012 .

[18]  Christopher D. Wickens,et al.  ATTENTION TO SAFETY AND THE PSYCHOLOGY OF SURPRISE , 2001 .

[19]  Natasha Merat,et al.  Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis , 2017, Hum. Factors.

[20]  K. Mosier,et al.  Human Decision Makers and Automated Decision Aids: Made for Each Other? , 1996 .

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

[22]  Mauricio Muñoz,et al.  Investigating the correspondence between driver head position and glance location , 2018, PeerJ Comput. Sci..

[23]  Wolfgang Birk,et al.  Collision warning with auto brake , 2006 .

[24]  Mark Vollrath,et al.  How to present collision warnings at intersections?--a comparison of different approaches. , 2013, Accident; analysis and prevention.

[25]  N A Stanton,et al.  Transition to manual: Comparing simulator with on-road control transitions. , 2017, Accident; analysis and prevention.

[26]  D. Norman,et al.  Memory And Attention , 1968 .

[27]  G. Gibson.  Hints of hidden heritability in GWAS , 2010, Nature Genetics.

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

[29]  Dick de Waard,et al.  A simple procedure for the assessment of acceptance of advanced transport telematics , 1997 .

[30]  Mark Mulder,et al.  The Effect of Haptic Support Systems on Driver Performance: A Literature Survey , 2015, IEEE Transactions on Haptics.

[31]  H. Grice.  Logic and conversation , 1975 .

[32]  Lorrie Faith Cranor,et al.  A Framework for Reasoning About the Human in the Loop , 2008, UPSEC.

[33]  Susan G. Hill,et al.  Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary? In A , 1989 .

[34]  Neville A. Stanton,et al.  The chatty co-driver: A linguistics approach applying lessons learnt from aviation incidents , 2017 .

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

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

[37]  Andreas Haslbeck,et al.  Flying the Needles , 2016, Hum. Factors.

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

[39]  Klaus Bengler,et al.  A transforming steering wheel for highly automated cars , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[40]  Wendy Ju,et al.  Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[41]  Charles Spence,et al.  Tactile warning signals for in-vehicle systems. , 2015, Accident; analysis and prevention.

[42]  Klaus Bengler,et al.  The influence of age on the take-over of vehicle control in highly automated driving , 2016 .

[43]  Alois Ferscha,et al.  Augmented reality navigation systems , 2006, Universal Access in the Information Society.

[44]  E. Russell,et al.  Memory and attention. , 1992, Journal of clinical psychology.

[45]  Victoria A Banks,et al.  What the drivers do and do not tell you: using verbal protocol analysis to investigate driver behaviour in emergency situations , 2014, Ergonomics.

[46]  Kathrin Zeeb,et al.  What determines the take-over time? An integrated model approach of driver take-over after automated driving. , 2015, Accident; analysis and prevention.

[47]  David C. Foyle,et al.  Attentional Issues with Superimposed Symbology: Formats for Scene-Linked Displays , 1995 .

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

[49]  William Payre,et al.  Fully Automated Driving , 2016, Hum. Factors.

[50]  Boris Israel.  Potenziale eines kontaktanalogen Head-up Displays für den Serieneinsatz , 2013 .

[51]  Neville A. Stanton,et al.  Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control , 2017, Hum. Factors.

[52]  Bruce N. Walker,et al.  Auditory Displays for In-Vehicle Technologies: , 2011 .

[53]  Neville A. Stanton,et al.  Encouraging Eco-Driving With Visual, Auditory, and Vibrotactile Stimuli , 2017, IEEE Transactions on Human-Machine Systems.

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

[55]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[56]  John A. Michon,et al.  A critical view of driver behavior models: What do we know , 1985 .

[57]  Christopher D. Wickens,et al.  Target Cuing in Visual Search: The Effects of Conformality and Display Location on the Allocation of Visual Attention , 1999, Hum. Factors.

[58]  Bobbie D. Seppelt,et al.  Making adaptive cruise control (ACC) limits visible , 2007, Int. J. Hum. Comput. Stud..

[59]  Mattias Bengtsson,et al.  Collision Warning with Full Auto Brake and Pedestrian Detection - a practical example of Automatic Emergency Braking , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[60]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[61]  Stefan Bauer,et al.  Acting together by mutual control: Evaluation of a multimodal interaction concept for cooperative driving , 2014, 2014 International Conference on Collaboration Technologies and Systems (CTS).

[62]  Fabrice Vienne,et al.  Evaluation of human-machine cooperation applied to lateral control in car driving , 2006 .

[63]  Kathleen L. Mosier,et al.  Does automation bias decision-making? , 1999, Int. J. Hum. Comput. Stud..

[64]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

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

[66]  D. Sperber,et al.  Relevance: Communication and Cognition , 1997 .

[67]  C. Baber.  Psychological aspects of conventional in-car warning devices , 1995 .

[68]  R. Gibberd,et al.  Epidemiology of medical error , 2000, BMJ : British Medical Journal.

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

[70]  Klaus Bengler,et al.  The design of a vibrotactile seat for conveying take-over requests in automated driving , 2017 .