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| Content Provider | IET Digital Library |
|---|---|
| Author | Diederichs, Frederik Knauss, Alessia Wilbrink, Marc Lilis, Yannis Chrysochoou, Evangelia Anund, Anna Bekiaris, Evangelos Nikolaou, Stella Finér, Svitlana Zanovello, Luca Maroudis, Pantelis Krupenia, Stas Absér, Andreas Dimokas, Nikos Apoy, Camilla Karlsson, Johan Larsson, Annika Zidianakis, Emmanouil Efa, Alexander Widlroither, Harald Dai, Mengnuo Teichmann, Daniel Sanatnama, Hamid Wendemuth, Andreas Bischoff, Sven |
| Abstract | Automated vehicles are entering the roads and automation is applied to cars, trucks, buses, and even motorcycles today. High automation foresees transitions during driving in both directions. The driver and rider state become a critical parameter since reliable automation allows safe intervention and transit control to the automation when manual driving is not performed safely anymore. When the control transits from automation to manual an appropriate driver state needs to be identified before releasing the automated control. The detection of driver states during manual and automated driving and an appropriate design of the human–machine interaction (HMI) are crucial steps to support these transitions. State-of-the-art systems do not take the driver state, personal preferences, and predictions of road conditions into account. The ADAS&ME project, funded by the H2020 Programme of the European Commission, proposes an innovative and fully adaptive HMI framework, able to support driver/rider state monitoring-based transitions in automated driving. The HMI framework is applied in the target vehicles: passenger car, truck, bus, and motorcycle, and in seven different use cases. |
| Starting Page | 889 |
| Ending Page | 899 |
| Page Count | 11 |
| ISSN | 1751956X |
| Volume Number | 14 |
| e-ISSN | 17519578 |
| Issue Number | Issue 8, Aug (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/14/8 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5342 |
| Journal | IET Intelligent Transport Systems |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2020-04-28 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Adaptive Transitions Appropriate Driver State Automated Control Automated Driving Automated Vehicle Automobile Automobile Electronics Automotive Component Automotive Electronics Automotive Engineering Busses Cars Control Transits Driver Information System High Automation Foresees Transitions Human Computer Interaction Manual Driving Motorcycle Motorcycles Road Safety Road Traffic Control Road Traffic System Control Road Vehicle State-of-the-art Society Traffic Engineering Computing Truck User InterFace |
| Content Type | Text |
| Resource Type | Article |
| Subject | Law Transportation Environmental Science Mechanical Engineering |
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