More Like this

1. Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

   https://doi.org/10.1177/1071181320641484  

   Ma, Zheng; Zhang, Yiqi (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors. 

   more » « less
2. Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue

   https://doi.org/10.1177/00187208241295932  

   Wei, Ran; McDonald, Anthony_D; Mehta, Ranjana_K; Garcia, Alfredo (November 2024, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   ObjectiveOur objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. BackgroundControl transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. MethodWe used data from a driving simulation to develop an active inference model of takeover performance. After validating the model’s predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. ResultsThe model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. ConclusionThe results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. ApplicationThe active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety. 

   more » « less
3. Evaluating the dissociation between drivers’ self-perceived and objective need for vehicle assistance during obstacle avoidance tasks

   https://doi.org/10.1177/21695067231192620  

   Luster, Maya S; Pitts, Brandon J (September 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Driver-assistance systems are becoming more commonplace; however, the realized safety benefits of these technologies depend on whether a person accepts and adopts automated driving aids. One challenge to adoption could be a preference-performance dissociation (PPD), which is a mismatch between a self-perceived desire and an objective need for assistance. Research has reported PPD in driving but has not extensively leveraged driving performance data to confirm its existence. Thus, the goal of this study was to compare drivers’ self-reported need for vehicle assistance to their objective driving performance. Twenty-one participants drove on a simulated road and traversed challenging, real-world roadway obstacles. Afterwards, they were asked about their preference for automated vehicle assistance (e.g., steering and braking) during their drive. Overall, some participants exhibited PPD that included both over- and underestimating their need for a particular type of automated assistance. Findings can be used to develop shared control and adaptive automation strategies tailored to particular users and contexts across various safety-critical environments. 

   more » « less
4. Nameability predicts subjective and objective measures of visual similarity

   Zettersten, M.; Suffill, E.; Lupyan, G. (January 2020, Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society)

   Denison, S.; Mack, M.; Xu, Y.; Armstrong, B.C. (Ed.)

   Do people perceive shapes to be similar based purely on their physical features? Or is visual similarity influenced by top-down knowledge? In the present studies, we demonstrate that top-down information – in the form of verbal labels that people associate with visual stimuli – predicts visual similarity as measured using subjective (Experiment 1) and objective (Experiment 2) tasks. In Experiment 1, shapes that were previously calibrated to be (putatively) perceptually equidistant were more likely to be grouped together if they shared a name. In Experiment 2, more nameable shapes were easier for participants to discriminate from other images, again controlling for their perceptual distance. We discuss what these results mean for constructing visual stimuli spaces that are perceptually uniform and discuss theoretical implications of the fact that perceptual similarity is sensitive to top-down information such as the ease with which an object can be named. 

   more » « less
5. Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

   https://doi.org/10.1177/00187208211034716  

   Yang, X Jessie; Schemanske, Christopher; Searle, Christine (August 2023, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   ObjectiveWe examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. BackgroundMost existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. MethodSeventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. ResultsOutcome bias and contrast effect significantly influence human operators’ trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him/herself. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. ConclusionHuman operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. ApplicationUnderstanding the trust adjustment process enables accurate prediction of the operators’ moment-to-moment trust in automation and informs the design of trust-aware adaptive automation. 

   more » « less