Driving simulator scenarios and measures to faithfully evaluate risky driving behavior: A comparative study of different driver age groups

Abstract

To investigate the links between mental workload, age and risky driving, a cross-sectional study was conducted on a driving simulator using several established and some novel measures of driving ability and scenarios of varying complexity. A sample of 115 drivers was divided into three age and experience groups: young inexperienced (18–21 years old), adult experienced (25–55 years old) and older adult (70–86 years old). Participants were tested on three different scenarios varying in mental workload from low to high. Additionally, to gain a better understanding of individuals’ ability to capture and integrate relevant information in a highly complex visual environment, the participants’ perceptual-cognitive capacity was evaluated using 3-dimensional multiple object tracking (3D-MOT). Results indicate moderate scenario complexity as the best suited to highlight well-documented differences in driving ability between age groups and to elicit naturalistic driving behavior. Furthermore, several of the novel driving measures were shown to provide useful, non-redundant information about driving behavior, complementing more established measures. Finally, 3D-MOT was demonstrated to be an effective predictor of elevated crash risk as well as decreased naturally-adopted mean driving speed, particularly among older adults. In sum, the present experiment demonstrates that in cases of either extreme high or low task demands, drivers can become overloaded or under aroused and thus task measures may lose sensitivity. Moreover, insights from the present study should inform methodological considerations for future driving simulator research. Importantly, future research should continue to investigate the predictive utility of perceptual-cognitive tests in the domain of driving risk assessment.

Altered driving performance of symptomatic Huntington’s disease gene carriers in simulated road conditions

“…Both low-and high-demand situations can result in too much mental workload and affect driving performance (Paxion et al 2014). A recent study, however, observed limited effects of age and driving experience in simulated urban driving (Michaels et al 2017). The authors suggested that urban driving increases mental workload and that this effect is similar for experienced and inexperienced drivers.…”

Effects of aging on foot pedal responses to visual stimuli

“…This means that measured driving ability and detected degradation by simulators depend on the complexity of the driving scenario. In fact, using driving scenarios with different complexity, Michaels et al [12] observed that the scenario with medium complexity was best suited to detect differences in driving ability between age groups. These suggest that a simple, scenario-independent task of appropriate complexity may be rather suitable for extracting and quantifying the absolute changes in physiological characteristics behind the decline in driving ability with age.…”

“…Earlier studies using driving simulators have reported the ample evidence of age-related decline in driving ability and an increase in crash risk [4][5][6][7][8][9][10][11]. In driving simulators based on realistic scenarios, however, pedal operations causing a car runaway that can lead to fatal accidents are rarely triggered, making accurate quantitative assessment difficult [12]. To quantitatively characterize the pedal operation errors, methods that can induce serious pedal errors effectively and more frequently are necessary.…”

Combining 3D-MOT with motor and perceptual decision-making tasks: conception of a life-sized virtual perceptual-cognitive training paradigm

“…The paradigm has been shown to be very sensitive to sport expertise and has demonstrated evidence of transfer across different populations and domains including sports (Legault and Faubert 2012, Faubert 2013, Parsons, Magill et al 2016, Romeas, Guldner et al 2016, Vartanian, Coady et al 2016. The main advantages of this noncontextual technique are that it is simple to use, malleable in terms of development, allows major gains with minimal training time and can be generalized to a variety of sports or environments (Faubert 2013, Mangine, Hoffman et al 2014, Harenberg, McCaffrey et al 2016, Romeas, Guldner et al 2016, Hoke, Reuter et al 2017, Michaels, Chaumillon et al 2017). However, this training technique lacks specificity and does not include the combination of perception and action that would be typically present in the sport environment.…”