Adaptive transitions for automation in cars, trucks, buses and motorcycles. Issue 8 (30th June 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 8(2020)
• Page Start: 889
• Page End: 899
• Publication Date: 2020-06-30
• Subjects: human computer interaction -- automotive engineering -- user interfaces -- motorcycles -- automobiles -- driver information systems -- automotive components -- road vehicles -- traffic engineering computing -- road traffic control -- automotive electronics -- road safety; adaptive transitions -- cars -- truck -- busses -- motorcycle -- automated vehicles -- high automation foresees transitions -- manual driving -- control transits -- appropriate driver state -- automated control -- automated driving -- state‐of‐the‐art Society; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5342
• Languages: English
• ISSNs: 1751-956X

Abstract

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.