The role of the driver is changing now that vehicles with driving automation technologies appear on the road. It evolves from being an active controller of the vehicle to being a supervisor of the automated ride. The driver has to collaborate with the driving automation and remains responsible for transitions in control, in particular for changing back to manual control. In the context of automated congestion driving this research developed and analysed driver support in the form of a control transition strategy that issues soft and hard warnings based on lane changes by surrounding traffic. The strategy models the generation of a specific type of warning as well as the timing of issuing. The research comprises the analysis of a naturalistic driving study that built the initial input for the development of the control transition strategy. As part of an iterative development process, the control transition strategy was subjected to a series of participant based evaluations. The two driving simulator experiments as well as a small scale field test revealed that the control transition strategy provides valuable driver support for automated congestion driving technology.
|Award date||7 Sep 2016|
|Place of Publication||Enschede|
|Publication status||Published - 7 Sep 2016|