A survey on knowledge and perceptions of advanced driver assistance systems in Massachusetts drivers

Hungund, Apoorva Pramod; Pradhan, Anuj K.

doi:10.1080/15389588.2022.2125228

Cited by 2 publications

(1 citation statement)

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“…Further, cultural difference has been identified as a potentially influential factor in users’ trust in automation ( Hoff and Bashir, 2015 ). Previous studies on drivers’ mental model as well as trust toward driving automation mainly targeted toward users in North America (e.g., DeGuzman and Donmez, 2021a ; Greenwood et al, 2022 ; Hungund and Pradhan, 2022 ) or Europe (e.g., Larsson, 2012 ; Beggiato and Krems, 2013 ; Beggiato et al, 2015 ). Given the rapidly increasing market share of advanced Level-2 automation in China ( Ministry of Industry and Information Technology, 2022 ), the increasing number of ADAS-related accidents in China ( Autoevolution, 2021 ), and the potential cultural difference in Asia ( Zaheer and Zaheer, 2006 ), understanding of Chinese drivers’ mental model of ADAS may provide valuable insights on the design of customized in-vehicle interfaces and driver education programs.…”

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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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