Abstract
The acceptance and hence the spread of automated and connected driving (ACD) systems is largely determined by the degree of subjective un-/certainty that users feel when interacting with automated vehicles. User acceptance is negatively influenced in particular by feelings of uncertainty when interacting with automated vehicles. The AutoAkzept project (which full title translates to: Automation without uncertainty to increase the acceptance of automated and connected driving) develops solutions of user-focused automation that place the vehicle occupants at the center of system development and thus reduce their uncertainty. Systems with user-focused automation use various sensors to detect uncertainty and its contributing factors (e.g. stress, kinetosis, and activity) in real time, integrate this information with context data and derive the current needs of the vehicle occupants. For this purpose, the project AutoAkzept develops an integrated architecture for context-sensitive user modelling, derivation of user demands and adaptation of system functions (e.g. human-machine-interaction, interior, driving styles). The architecture is implemented using machine learning methods to develop real-time algorithms that map situational contexts, user states and adaptation requirements. The overall objective of AutoAkzept is the development of promising adaptation strategies to improve the user experience based on the identified uncertainty related needs. By reducing or preventing subjective uncertainties, the developments of the project thus ensure a positive, comfortable user experience and contribute to increasing the acceptance of ACD.
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Acknowledgment
The authors gratefully acknowledge the financial funding of this work by the German Federal Ministry of Transport and Digital Infrastructure under the grants 16AVF2126A, 16AVF2126B, and 16AVF2126D.
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Drewitz, U. et al. (2020). Towards User-Focused Vehicle Automation: The Architectural Approach of the AutoAkzept Project. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. Automated Driving and In-Vehicle Experience Design. HCII 2020. Lecture Notes in Computer Science(), vol 12212. Springer, Cham. https://doi.org/10.1007/978-3-030-50523-3_2
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