Fully Automated Driving

Objective: An experiment was performed in a driving simulator to investigate the impacts of practice, trust, and interaction on manual control recovery (MCR) when employing fully automated driving (FAD). Background: To increase the use of partially or highly automated driving efficiency and to improve safety, some studies have addressed trust in driving automation and training, but few studies have focused on FAD. FAD is an autonomous system that has full control of a vehicle without any need for intervention by the driver. Method: A total of 69 drivers with a valid license practiced with FAD. They were distributed evenly across two conditions: simple practice and elaborate practice. Results: When examining emergency MCR, a correlation was found between trust and reaction time in the simple practice group (i.e., higher trust meant a longer reaction time), but not in the elaborate practice group. This result indicated that to mitigate the negative impact of overtrust on reaction time, more appropriate practice may be needed. Conclusions: Drivers should be trained in how the automated device works so as to improve MCR performance in case of an emergency. Application: The practice format used in this study could be used for the first interaction with an FAD car when acquiring such a vehicle.

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

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

[3]  Edward M. Hitchcock,et al.  Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. , 2013, Journal of experimental psychology. Applied.

[4]  Nils Petter Gregersen,et al.  FROM CONTROL OF THE VEHICLE TO PERSONAL SELF-CONTROL; BROADENING THE PERSPECTIVES TO DRIVER EDUCATION , 2002 .

[5]  Carryl L. Baldwin,et al.  Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies , 2009 .

[6]  Christopher D. Wickens,et al.  Workload and Reliability of Predictor Displays in Aircraft Traffic Avoidance , 2000 .

[7]  G Nirschl,et al.  DRIVER-VEHICLE INTERACTION WHILE DRIVING WITH ACC IN BORDERLINE SITUATIONS , 1997 .

[8]  T. Inagaki,et al.  Design of human–machine interactions in light of domain-dependence of human-centered automation , 2006, Cognition, Technology & Work.

[9]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[10]  Marika Hoedemaeker,et al.  Driver behavior in an emergency situation in the Automated Highway System , 1999 .

[11]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[12]  Raja Parasuraman,et al.  Trust in Decision Aids: a Model and Its Training Implications , 1998 .

[13]  William Payre,et al.  Intention to use a fully automated car: attitudes and a priori acceptability , 2014 .

[14]  David B. Kaber,et al.  Out‐of‐the‐loop performance problems and the use of intermediate levels of automation for improved control system functioning and safety , 1997 .

[15]  Joel S. Warm,et al.  Development of Active and Passive Fatigue Manipulations Using a Driving Simulator , 2007 .

[16]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[17]  John D. Lee,et al.  Trust, self-confidence, and operators' adaptation to automation , 1994, Int. J. Hum. Comput. Stud..

[18]  Lisanne Bainbridge,et al.  Ironies of automation , 1982, Autom..

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

[20]  Mark S. Young,et al.  Cooperation between drivers and automation: implications for safety , 2009 .

[21]  Karel Brookhuis,et al.  Consequences of automation for driver behaviour and acceptance. , 2006 .

[22]  Raja Parasuraman,et al.  Complacency and Bias in Human Use of Automation: An Attentional Integration , 2010, Hum. Factors.

[23]  Jean-Michel Hoc,et al.  EMPIRICAL STUDIES RECHERCHES EMPIRIQUES HUMAN-MACHINE COOPERATION IN CAR DRIVING FOR LATERAL SAFETY : DELEGATION AND MUTUAL CONTROL , 2006 .

[24]  Mark S. Young,et al.  Vehicle automation and driving performance , 1998 .

[25]  Bonnie M. Muir,et al.  Trust Between Humans and Machines, and the Design of Decision Aids , 1987, Int. J. Man Mach. Stud..

[26]  N A Stanton,et al.  What's skill got to do with it? Vehicle automation and driver mental workload , 2007, Ergonomics.

[27]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[28]  N. Moray,et al.  Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. , 1996, Ergonomics.

[29]  Regina A. Pomranky,et al.  The role of trust in automation reliance , 2003, Int. J. Hum. Comput. Stud..

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

[31]  Hermann Winner,et al.  Adaptive cruise control field operational test–the learning phase , 2001 .

[32]  Paul R. Sackett,et al.  OUTLIER DETECTION AND TREATMENT IN I/O PSYCHOLOGY: A SURVEY OF RESEARCHER BELIEFS AND AN EMPIRICAL ILLUSTRATION , 2006 .

[33]  Hiroshi Furukawa,et al.  Computer simulation for the design of authority in the adaptive cruise control systems under possibility of driver's over-trust in automation , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).