Yannis Lilis, Emmanouil Zidianakis, Nikolaos Partarakis, S. Ntoa, C. Stephanidis
{"title":"A Framework for Personalised HMI Interaction in ADAS Systems","authors":"Yannis Lilis, Emmanouil Zidianakis, Nikolaos Partarakis, S. Ntoa, C. Stephanidis","doi":"10.5220/0007801505860593","DOIUrl":null,"url":null,"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 vehicle 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.","PeriodicalId":218840,"journal":{"name":"International Conference on Vehicle Technology and Intelligent Transport Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Vehicle Technology and Intelligent Transport Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0007801505860593","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
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 vehicle 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.