A Driving Simulator Study Exploring the Effect of Different Mental Models on ADAS System Effectiveness

Rossi, R.; Gastaldi, Massimiliano; Biondi, Francesco; Orsini, Federico; De, Giulia; Mulatti, Claudio

doi:10.1007/978-3-030-58465-8_7

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…The past few years have witnessed an increasing number of ADAS-related crashes (National Highway Traffic Safety Administration, 2022), most of which are due to or partially due to drivers’ misusing or overusing of advanced driver assistance systems (ADAS). Previous studies found that drivers who have inappropriate mental models of ADAS (i.e., understanding of the functions and limitations of ADAS) were more likely to misuse or overuse the systems (Dickie & Boyle, 2009; Rossi et al, 2020). Hence, researchers tried to understand the factors influencing drivers’ mental model of ADAS, which can provide insights on how to help drivers construct appropriate ADAS mental models.…”

Section: Introductionmentioning

confidence: 99%

Using Sensitivity and Bias in Signal Detection Theory to Predict Proportion Correctness: Simulation and Case Study on ADAS Mental Model Evaluation

Huang,

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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The sensitivity ( d’) and bias ( c) in signal detection theory can reflect respondents’ objective performance in understanding a system and their bias towards saying yes. Thus, d’ and c can be used as alternatives of proportion correctness (PC) when evaluating drivers’ mental models of advanced driving assistance system (ADAS). The adoption of d’ and c as mental model metrics also allows cross-study comparisons as their values are independent of signal (i.e., ADAS function present) and noise (i.e., ADAS function absent) ratio. However, there is no closed-form solution of the relationships among d’, c, and PC. Hence, using numerical simulations, we extracted an empirical equation that quantifies how d’ and c can estimate PC. The equation was then validated based on participants’ responses from a survey study that targeted towards drivers’ ADAS mental model. The results show that the empirical equation reached a satisfying performance (R2 > 0.8) in estimating PC.

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Section: Introductionmentioning

confidence: 99%

Using Sensitivity and Bias in Signal Detection Theory to Predict Proportion Correctness: Simulation and Case Study on ADAS Mental Model Evaluation

Huang,

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…With the rapid development of technologies, advanced driver assistance systems (ADAS) have become increasingly prevalent worldwide over the past decade, with a prior motivation for improving driving safety (Banks & Stanton, 2016). However, the misuse or overuse of ADAS has led to a large number of accidents in the past few years (National Highway Traffic Safety Administration, 2022) and drivers’ inappropriate mental models of ADAS (i.e., driver knowledge of the functions and limitations of ADAS) were identified as one of the reasons leading to misuse or overuse of the ADAS (Dickie & Boyle, 2009; Rossi et al, 2020). Therefore, a number of studies have been conducted to understand drivers’ ADAS mental models and explored how to help drivers construct appropriate mental models of ADAS.…”

Section: Introductionmentioning

confidence: 99%

Assessing Drivers’ Mental Model Of Advanced Driver Assistance Systems Using Signal Detection Theory

Huang,

Yan,

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Previous studies evaluated drivers’ knowledge of advanced driver assistance systems (ADAS) using different kinds of percent-correctness-based mental model scores (MMS), which makes cross-study comparisons difficult. To resolve this issue, our study explored the use of sensitivity (i.e., d-prime ( d’)) and response bias (i.e., criterion location ( c)) in signal detection theory (SDT) as a measure of drivers’ ADAS mental models. Based on the data collected from a survey among 287 ADAS users, regression models were fitted, and it was found that d’ and c accounted for a large variance when estimating drivers’ ADAS mental models as measured by MMSs (adjusted R2 > 0.8). Further, predictors of MMSs were also predictors of d’ and c, but d’ and c include additional information that was not covered in MMSs. These findings support the usage of d’ and c as standard metrics for assessing drivers’ ADAS mental models in future research.

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“…Thus, users’ knowledge of and trust in ADAS may still play a critical role in driving safety. To systematically evaluate users’ knowledge of ADAS, previous studies used the concept of ADAS mental model and defined it as drivers’ understanding of the functions, limitations, and capabilities of ADAS (e.g., Beggiato and Krems, 2013 ; Beggiato et al, 2015 ; Rossi et al, 2020 ; Pai et al, 2023 ). However, to the best of our knowledge, most existing research mainly focused on traditional ADAS technologies (e.g., Beggiato and Krems, 2013 ; Beggiato et al, 2015 ; DeGuzman and Donmez, 2021a , b ; Lubkowski et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…To secure the benefits of ADAS, drivers need to have an appropriate understanding of how, when, and whether ADAS can be used. Specifically, drivers’ mental models of traditional Level-2 ADAS (i.e., knowledge of the functions and limitations of the ADAS) were found to be associated with driving safety, when either the driving automation was responsible for the longitudinal control (i.e., through ACC) or lateral control of the vehicles (i.e., through LCC) ( Dickie and Boyle, 2009 ; Rossi et al, 2020 ; Gaspar et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Beyond adaptive cruise control and lane centering control: drivers’ mental model of and trust in emerging ADAS technologies

Huang

Wen

et al. 2023

Front. Psychol.

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IntroductionThe potential safety benefits of advanced driver assistance systems (ADAS) highly rely on drivers’ appropriate mental models of and trust in ADAS. Current research mainly focused on drivers’ mental model of adaptive cruise control (ACC) and lane centering control (LCC), but rarely investigated drivers’ understanding of emerging driving automation functions beyond ACC and LCC.MethodsTo address this research gap, 287 valid responses from ADAS users in the Chinese market, were collected in a survey study targeted toward state-of-the-art ADAS (e.g., autopilot in Tesla). Through cluster analysis, drivers were clustered into four groups based on their knowledge of traditional ACC and LCC functions, knowledge of functions beyond ACC and LCC, and knowledge of ADAS limitations. Predictors of driver grouping were analyzed, and we further modeled drivers’ trust in ADAS.ResultsDrivers in general had weak knowledge of LCC functions and functions beyond ACC and LCC, and only 27 (9%) of respondents had a relatively strong mental model of ACC and LCC. At the same time, years of licensure, weekly driving distance, ADAS familiarity, driving style (i.e., planning), and personability (i.e., agreeableness) were associated with drivers’ mental model of ADAS. Further, it was found that the mental model of ADAS, vehicle brand, and drivers’ age, ADAS experience, driving style (i.e., focus), and personality (i.e., emotional stability) were significant predictors of drivers’ trust in ADAS.DiscussionThese findings provide valuable insights for the design of driver education and training programs to improve driving safety with ADAS.

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A Driving Simulator Study Exploring the Effect of Different Mental Models on ADAS System Effectiveness

Cited by 11 publications

References 11 publications

Using Sensitivity and Bias in Signal Detection Theory to Predict Proportion Correctness: Simulation and Case Study on ADAS Mental Model Evaluation

Using Sensitivity and Bias in Signal Detection Theory to Predict Proportion Correctness: Simulation and Case Study on ADAS Mental Model Evaluation

Assessing Drivers’ Mental Model Of Advanced Driver Assistance Systems Using Signal Detection Theory

Beyond adaptive cruise control and lane centering control: drivers’ mental model of and trust in emerging ADAS technologies

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