Drivers’ Mental Workload In Agricultural Semi-Autonomous Vehicles

Abstract

Drivers' mental workload when using driving assistant systems and in-vehicle automation has been the subject of many studies in recent years. Drivers of semi-autonomous agricultural vehicles are experiencing an increasing number of automated systems. Due to implications of automation support on the operators' performance, a human factors perspective is needed to identify the consequences of such automated systems. In this simulator study, the effects of vehicle steering and implement monitoring and control automation were investigated using a tractor air-seeder system as a case study. Experiments were conducted using the tractor air-seeder driving simulator (TAS-DS) located in the Agricultural Ergonomics Laboratory at the University of Manitoba. Study participants were university students with tractor driving experience. Based on the results from the experiment, most of the automation conditions impose moderate levels of mental workload on operators. Implement monitoring and control automation show significant effect on the drivers' mental workload, contrary to the steering automation. Mental Workload and Driving Task Beside the benefits, automated driving tasks also introduce new problems over the human operator (Stanton and Marsden, 1996). Drivers' mental workload is one of these problems. Mental workload reflects perceptual and cognitive demands of

Impact of Automation on Drivers’ Performance in Agricultural Semi-Autonomous Vehicles

“…A global score of mental workload imposed by the task was calculated by averaging the scores of these parameters. For more information on the DALI used in this experiment, refer to Bashiri and Mann (2013).…”

“…Considering the differences that exist between on-road driving and in-field operation, as described by Bashiri and Mann (2013), results from studying on-road vehicles cannot be applied to agricultural vehicles. Identifying the effects of new automated systems on drivers' mental workload in agricultural vehicles demands specific studies.…”

“…The hypothesis was that increasing the automation support level would reduce the reaction time of the operator and increase the accuracy of the operator's actions. The results from a self-assessment technique, reported by Bashiri and Mann (2013), were used to identify any possible correlations between mental workload measures.…”

Drivers Information Gathering Pattern During Transitions to Manual Control: A Study About Hmi Design for Autonomous Vehicles

“…This contributes to a gradual degeneration of the vigilance of the system (Zeeb et al, 2015;Loon & Martens, 2015;Terai et al, 2015;Payre , Cestac & Delhomme, 2014;Aziz, Hiroguchi & Sawaragi, 2013;Brookhius & Waard, 2009;Maltz & Shinar, 2007;Rudim-Brown & Parker, 2004;Rudin-Brown & Jamson, 2013;Louw et al, 2015;De Winter, Happee, Martens & Stanton, 2014;Schiben et al, 2014;Stockert, Richadson & Lienkamp,2015;Hergeth et al, 2016;Parasuraman & Manzey, 2010;Wickens et al, 2015;Ma & Kaber, 2006;Neubauer et al, 2012; Bashiri & Mann, 2013;Young & Stanton, 2000;Fletcher & Zelinsky, 2009;Allahyar et al, 2016;Young & Stanton, 2002;Sheridan, 1999).…”

“…Most of the literature categorically states that the more automated the task, the greater the apathy of the driver. As a consequence for that, the faster the process of loss of vigilance (Lu & De Winter, 2015;Strand et al, 2014;Terai et al, 2015;Weyer et al, 2014;Rudin-Brown & Parker, 2004;Rudin-Brown & Jamson.,2013;Louw et al, 2015;De Winter et al,2014;Banks & Stanton, 2013;Stockert et al, 2015;Terai et al, 2015;Gonçalves & Bengler, 2015;Hergeth et al, 2016;Shen & Neyens, 2014;Payre et al, 2016;Neubauer, Matthews & Saxby, 2012;Mulder, Abbink & Boer, 2012;Bashiri & Mann, 2013;Young & Stanton, 2000;Allahyar et al, 2016;Young & Stanton, 2002;Sheridan, 1999).…”