Autonomous Control in Military Logistics Vehicles: Trust and Safety Analysis

Ground vehicles are increasingly designed to incorporate autonomous control for better performance, control and efficiency. Such control is particularly critical for military logistics vehicles where drivers are carrying sensitive loads through potentially threatening routes. It is imperative therefore to evaluate what role does autonomy play to help safety, and whether drivers trust autonomous control. In this paper we investigate the use of semi-autonomous vehicles used for military logistics and carry out human factors analysis to reflect on trust and safety issues that emerge from the driving of such vehicles.

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

[2]  Guy H. Walker,et al.  Designer driving: drivers' conceptual models and level of trust in adaptive cruise control , 2007 .

[3]  Jean Scholtz,et al.  Operator interventions in autonomous off-road driving: effects of terrain , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

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

[5]  Tal Oron-Gilad,et al.  Road Characteristics and Driver Fatigue: A Simulator Study , 2007, Traffic injury prevention.

[6]  Antonio Cerone Closure and Attention Activation in Human Automatic Behaviour: A Framework for the Formal Analysis of Interactive Systems , 2011 .

[7]  Jacek Gwizdka Using stroop task to assess cognitive load , 2010, ECCE.

[8]  Neville A Stanton,et al.  Driver behaviour with adaptive cruise control , 2005, Ergonomics.

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

[10]  Antonio Cerone,et al.  A formal approach to human error recovery , 2007 .

[11]  Ani Nahapetian,et al.  Mobile Computing, Applications, and Services , 2011, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

[12]  Markvollrath,et al.  The influence of cruise control and adaptive cruise control on driving behaviour--a driving simulator study. , 2011, Accident; analysis and prevention.

[13]  Linda Ng Boyle,et al.  Drivers' Understanding of Adaptive Cruise Control Limitations , 2009 .

[14]  Duncan Robertson,et al.  Quantitative Physiological Assessment of Stress Via Altered Immune Functioning Following Interaction With Differing Automotive Interface Technologies , 2011, Int. J. Hum. Comput. Interact..

[15]  G. K. Shelton-Rayner,et al.  Quantifying exposure to psychological and physiological stress and automotive design , 2009 .

[16]  Annika F L Larsson,et al.  Driver usage and understanding of adaptive cruise control. , 2012, Applied ergonomics.

[17]  Annika F.L. Larsson,et al.  Issues in reclaiming control from advanced driver assistance systems , 2010 .

[18]  Philip Moore,et al.  Intelligent semi-autonomous vehicles in materials handling , 1999 .

[19]  Jessie Y C Chen,et al.  UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment , 2010, Ergonomics.

[20]  Ann Blandford,et al.  An approach to formal verification of human–computer interaction , 2007, Formal Aspects of Computing.

[21]  S.P. Hoogendoorn,et al.  Driving behavior interaction with ACC: results from a Field Operational Test in the Netherlands , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[22]  John Rushby,et al.  Using model checking to help discover mode confusions and other automation surprises , 2002, Reliab. Eng. Syst. Saf..

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

[24]  Padmanabhan Krishnan,et al.  A Framework for Analysing Driver Interactions with Semi-Autonomous Vehicles , 2012, FTSCS.

[25]  Martin L. Griss,et al.  Activity-Aware Mental Stress Detection Using Physiological Sensors , 2010, MobiCASE.