Authors:
Yannis Lilis
1
;
Emmanouil Zidianakis
1
;
Nikolaos Partarakis
1
;
Stavroula Ntoa
1
and
Constantine Stephanidis
2
Affiliations:
1
Institute of Computer Science, FORTH, N. Plastira 100 Vassilika Vouton, GR-700 13, Heraklion, Crete and Greece
;
2
Institute of Computer Science, FORTH, N. Plastira 100 Vassilika Vouton, GR-700 13, Heraklion, Crete, Greece, Department of Computer Science, University of Crete, Voutes Campus GR-700 13 Heraklion, Crete and Greece
Keyword(s):
ADAS Systems, Personalisation, Adaptation, Human Machine Interaction.
Abstract:
Personalisation features of Advanced Driver Assistant Systems (ADAS) can improve safety and driving experience. However, they are typically developed in an ad-hoc, application-specific and vehicle-specific manner, resulting in tightly coupled implementations that are difficult to extend, while disallowing reuse of personalisation code or even personalisation logic across different setups. In this context, this paper proposes a framework for supporting personalised HMI interaction in ADAS systems, developed in the context of the H2020 ADAS&ME project. The framework is based on a rule engine that uses a customisable and extensible set of personalisation and adaptation rules, provided by automotive domain and HMI experts, and evaluates them according to the driver, vehicle and environment to produce HMI activation and GUI personalisation and adaptation decisions. Personalised HMI modality selection is realised by taking into account all available input and output modalities of the vehic
le and maintaining bindings for their activation. At the same time, GUI personalisation is handled automatically through a GUI toolkit of personalisable and adaptable user controls that can be used for developing any GUI application requiring personalisation features. The paper presents the design and development of the framework and validates it by deploying it in two case studies.
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