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What Do You Want for In-Vehicle Agents? One Fits All vs. Multiple Specialized Agents., , , , , и . AutomotiveUI (Adjunct Proceedings), стр. 199-200. ACM, (2022)Manipulating Drivers' Mental Workload: Neuroergonomic Evaluation of the Speed Regulation N-Back Task Using NASA-TLX and Auditory P3a., , , , , и . AutomotiveUI (Adjunct Proceedings), стр. 145-149. ACM, (2023)Exploring Driver Responses to Authoritative Control Interventions in Highly Automated Driving., , , , , и . AutomotiveUI, стр. 145-155. ACM, (2023)How to Design Valid Simulator Studies for Investigating User Experience in Automated Driving: Review and Hands-On Considerations., , , , , и . AutomotiveUI, стр. 105-117. ACM, (2018)Workshop on Human-Vehicle-Environment Cooperation in Automated driving: The Next Stage of a Classic Topic., , , , и . AutomotiveUI (adjunct), стр. 200-203. ACM, (2021)Calibration of Trust Expectancies in Conditionally Automated Driving by Brand, Reliability Information and Introductionary Videos: An Online Study., , , и . AutomotiveUI, стр. 118-128. ACM, (2018)Introducing VAMPIRE - Using Kinaesthetic Feedback in Virtual Reality for Automated Driving Experiments., , , , , и . UI, стр. 204-214. ACM, (2022)Genie vs. Jarvis: Characteristics and Design Considerations of In-Vehicle Intelligent Agents., , , , и . AutomotiveUI (adjunct), стр. 197-199. ACM, (2021)The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency., , , и . Hum. Factors, (2020)Ambient Light Conveying Reliability Improves Drivers’ Takeover Performance without Increasing Mental Workload, , , , и . Multimodal Technologies and Interaction, 6 (9): 73 (2022)