Consumer demand for partial driving automation and hands-free driving capability

Mueller, Alexandra S.; Cicchino, Jessica B.; Calvanelli, Joseph V.

doi:10.1016/j.jsr.2022.11.012

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The current study was designed to examine acceptability of a generic camera-based driver monitoring system. A variety of different monitoring strategies are possible [77] , and some research has indicated that acceptance will vary as a function of monitoring strategy (e.g., steering wheel sensors versus camera-based monitoring, see [78] . Since DSM has not been widely-deployed, acceptability/acceptance is likely to change and evolve as drivers gain exposure to specific systems.…”

Section: Limitationsmentioning

confidence: 99%

Acceptability of Driver State Monitoring

Nees,

Tian

2024

Preprint

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Though widespread monitoring of driver states and behaviors appears to be imminent, research on perceived acceptability of driver state monitoring has lagged behind research on acceptance of automated driving per se. The current study used an original 29-item driver state monitoring acceptability scale to examine potential theoretical subdimensions of acceptability of driver state monitoring (DSM) systems with an exploratory factor analysis approach. The study used an online, U.S.-based sample. Three subdimensions of DSM acceptability emerged: (1) general acceptability of DSM; (2) concerns over DSM; and (3) perceived ease of use. Correlational analyses were conducted to investigate relationships of subdimensions of DSM acceptability with driving styles, accident concerns, perceived driving skill, tendency to speed, self-reported accident and violations history, and demographic variables. Results showed significant but weak relationships between subdimensions of DSM acceptability with a number of variables, including positive relationships with cautious driving styles, familiarity with advanced driver assistance technology (ADAS), and accidents and violations history. Negative relationships were found between acceptability and anxious driving styles, driving experience, and worries of being involved in car accidents. Female participants reported a lower level of acceptability than male participants, whereas current/previous DSM users reported a higher level of acceptability than non-users. Participants also were randomly assigned to receive descriptions indicating that DSM would occur during all driving periods or only during periods when automated systems were engaged. There were no significant differences in acceptability ratings for monitoring in the presence versus absence of automated driving.

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Section: Limitationsmentioning

confidence: 99%

Acceptability of Driver State Monitoring

Nees,

Tian

2024

Preprint

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“…By 2050, 95% of registered vehicles in the US are predicted to be equipped with partial-automation systems that are capable of simultaneously controlling vehicle speed and time headway (e.g., Adaptive Cruise Control (ACC)), as well as provide steering input to center the vehicle within the lane on a sustained basis (Highway Loss Data Institute, 2022a). The use of ACC has been shown to have safety benefits in terms of crash and injury reduction (Najm et al, 2006;Tan et al, 2021), yet it is unclear whether the combination of ACC and lane centering is associated with safety benefits (Highway Loss Data Institute, 2022b; Landry et al, 2020;Mueller et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The Relationship Between Partial-Automation Use and Driver Sentiment Toward Automation

Haus,

Gershon,

Mehler

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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In this study we utilize naturalistic driving data and semi-structured interviews from the MIT Advanced Vehicle Technology naturalistic driving study to explore the relationship between partial-automation use behaviors and driver sentiment by connecting automation use, prevalence of Elevated Driving Kinematic (EDK) events, and incidence of drops in steering support, with participant sentiments towards partial-automation performance and trust in partial-automation. We found that higher automation users expressed more positive statements about system use ( p = 0.012). Additionally, participants that experienced more EDKs during automation use expressed lower sentiments concerning automation performance. Participants that experienced more drops in automated steering support expressed lower trust in automation and lower sentiments toward automation performance. This work takes an important step in connecting driver sentiment to observed and measurable behaviors in real-world driving to aid in design of more useful and trustworthy automation.

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“…Various parties have raised concerns that driver assistance systems and Automated Driving Systems (ADS) may have unintended side effects, such as inappropriate driver trust and over-reliance on automation (Mueller et al, 2022). Organizations like NHTSA (2022) and the Dutch Safety Board (OVV, 2019) have reported and analyzed crashes and fatal accidents directly related to the use of-and overtrust in-this technology.…”

Section: Introductionmentioning

confidence: 99%

Trust in automated vehicles: constructs, psychological processes, and assessment

Walker,

Forster,

Hergeth

et al. 2023

Front. Psychol.

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There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: (1) experience plays a vital role in trust calibration; (2) experience should be measured not just in terms of distance traveled, but in terms of the range of situations encountered; (3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal.

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Consumer demand for partial driving automation and hands-free driving capability

Cited by 6 publications

References 28 publications

Acceptability of Driver State Monitoring

Acceptability of Driver State Monitoring

The Relationship Between Partial-Automation Use and Driver Sentiment Toward Automation

Trust in automated vehicles: constructs, psychological processes, and assessment

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