New societal requirements and functional growth put new demand on future driver information. Simultaneously, new technology and IT capabilities makes it possible to constantly adapt the information given to the driver for different reasons. Therefore, the aim of this thesis was to obtain an improved understanding, strengthen knowledge of the adaptive control of driver information to understand if, for what reason, when and where to use adaptive driver information (ADI). Also some possible new means to support drivers were suggested.The main purpose of driver information is to support the driver in achieving goals such as a safer, more environmentally friendly, more efficient, legal and enjoyable transportation by providing correct information and feedback.ADI can support the driver throughout development of skill and when performing operational, tactical, and strategic level tasks. Also tasks related to setting goals for the driving task and encouraging good driving behaviour can be supported. ADI can, furthermore help drivers to stay within their comfort zone by visualizing risk or certainty, identify and thereafter adapt how a message is communicated to different personalities, maintain the driver’s mental workload within the safe task load area by reducing demand when it is too high, increase mental workload by extra stimulating task during too low a mental demand, and minimize the risk for mismatches between effort and real demand.ADI changes automatically that may cause new and unpredictable issues reducing the purpose of driver information. These may include: mode confusion, function allocation, over and under trust, locus of control issues, skill degeneration and too low/high mental workload. Research has suggested that the most efficient way to reduce these issues is to make the driver and the automation (the agents) get along together and become team players. The team players should share goals, show intention, show limits of performance, state etcetera. However, for cars, a consumer product, in which visual demand is high, an approach can be where information vanishes when agents have become a “team”. This approach may be called “team building”.Research and industrial contributions has been presented. Several examples of how ADI can be carried out have been suggested and some even illustrated. / <p>Godkänd; 2014; 20141006 (stadav); Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Staffan Davidsson Ämne: Teknisk Psykologi/Engineering Psychology Avhandling: Adaptive Driver Information Opponent: Docent Forskningsledare Björn Peters, Statens väg- och transportforskningsinstitut, Linköping Ordförande: Professor Håkan Alm, Institutionen för ekonomi, teknik och samhälle, Luleå Tekniska Universitet Tid: Tisdag 11 november 2014, kl. 10.00 Plats: E243, Luleå tekniska universitet</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-25850 |
| Date | January 2014 |
| Creators | Davidsson, Staffan |
| Publisher | Luleå tekniska universitet, Arbetsvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, 1402-1544 |
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