Investigating Car Drivers’ Information Demand after Safety and Security Critical Incidents

Gröber, Lea Theresa; Fassl, Matthias; Gupta, Abhilash; Krombholz, Katharina

doi:10.1145/3411764.3446862

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…As Zepf et al [26] mentioned, an empathic interface on vehicles can prevent emotional distress from drivers and dangers such as car accidents, and as an empathic system, The concept of AutoMate makes drivers feel more calm and at the same time more responsible to help victims. Lastly, Gröber et al [12] , mentioned that a form of communication between car and driver can be helpful to prevent possible accidents. Therefore a communication between those two parties can help prevent worsen conditions from victims as well as building a trust between drivers and cars.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

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Automate - An Empathic First-Aid Communication System to Reduce the Bystander Effect in Car Accidents

Demir

Lang

Melibeu

et al. 2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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levels not sufce without a cognitive statement, and vice versa [23].Sometimes human empathy is declining into a more apathetic approach, especially in cases of an emergency. Especially in cases when other people are present, individuals do not ofer help, caused by the lack of feeling responsible and emotional attachment toward a victim. Sometimes they feel overwhelmed and afraid that they might do something wrong or worsen possible injuries.Situations such as bystanders ignoring accidents and subsequently injured victims are becoming way too common, and using mobile apps such as Waze [7] or Google maps, the driver navigates away from an accident to avoid these types of encounters. This leads not only to an apathetic approach of an accident, as victims can be considered as a nuisance rather than someone that needs help, but victims would be completely by themselves in these situations, and that is judgmental for the situation, as every lost minute could be crucial for someone's life.One possible solution for reducing bystander efects is through the means of an in-vehicle information system that supports the user when helping a car accident victim. We decided to design and implement a voice assistance system that helps drivers in such situations and research this approach in a simulator based user study. The guiding research question was: RQ: How can extended frst-aid knowledge available in a voice assistance system help bystanders empathize with victims of car accidents? Additionally, we were interested in the following sub questions: Sub-RQ1: How does sharing information about a nearby accident afect another person's behavior/emotions? Sub-RQ2: How can the communication system provide knowledge to reduce bystander cognitive dissonance? The design of AutoMate followed the assumption that proactively asking for a bystander from the perspective of the victim to come and help would create a more efective impact than merely displaying information. AutoMate informs drivers (bystanders) that an accident occurred and how far it was. With our research we provide an approach to how future frst-aid communication systems in cars could be designed.

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Section: Discussion and Limitationsmentioning

confidence: 99%

“…Gröber et al [12] mentioned that, with the automatization of cars, they are becoming more complex and with that, leading to new forms of accidents. They mentioned that having proper communication inside the car could help drivers understand the car's behavior, build trust in the technology and resolve possible problems [12].…”

Section: System Designmentioning

confidence: 99%

Automate - An Empathic First-Aid Communication System to Reduce the Bystander Effect in Car Accidents

Demir

Lang

Melibeu

et al. 2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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“…We mainly rely on quantitative measures investigating people's privacy decision-making in the CAV context. Future work could consider examining individuals' mental models [12] of CAV privacy by using qualitative methods such as open-ended questions [23] or interviews.…”

Section: Individual Difference In Privacy Decisions Of Cavsmentioning

confidence: 99%

Effects of Knowledge and Experience on Privacy Decision-Making in Connected Autonomous Vehicle Scenarios

Cai¹,

Xiong²

2022

Proceedings 2022 Symposium on Usable Security

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To enhance the acceptance of connected autonomous vehicles (CAVs) and facilitate designs to protect people's privacy, it is essential to evaluate how people perceive the data collection and use inside and outside the CAVs and investigate effective ways to help them make informed privacy decisions. We conducted an online survey (N = 381) examining participants' utility-privacy tradeoff and data-sharing decisions in different CAV scenarios. Interventions that may encourage safer data-sharing decisions were also evaluated relative to a control. Results showed that the feedback intervention was effective in enhancing participants' knowledge of possible inferences of personal information in the CAV scenarios. Consequently, it helped participants make more conservative data-sharing decisions. We also measured participants' prior experience with connectivity and driver-assistance technologies and obtained its influence on their privacy decisions. We discuss the implications of the results for usable privacy design for CAVs.

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Creepy Assistant: Development and Validation of a Scale to Measure the Perceived Creepiness of Voice Assistants

Phinnemore

Reza

Lewis

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Investigating Car Drivers’ Information Demand after Safety and Security Critical Incidents

Cited by 4 publications

References 25 publications

Automate - An Empathic First-Aid Communication System to Reduce the Bystander Effect in Car Accidents

Automate - An Empathic First-Aid Communication System to Reduce the Bystander Effect in Car Accidents

Effects of Knowledge and Experience on Privacy Decision-Making in Connected Autonomous Vehicle Scenarios

Creepy Assistant: Development and Validation of a Scale to Measure the Perceived Creepiness of Voice Assistants

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