Alexandra Neukum

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The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The(More)
Cooperative Conditionally Automated Driving (CAD) systems pose new challenges to the development of human-machine interfaces (HMI). The system's current status and intentions must be communicated unambiguously to ensure safe driver-system interaction and acceptance. This topic is becoming increasingly important as advanced automated driving functions are(More)
Increasingly complex in-vehicle information systems (IVIS) have become available in the automotive vehicle interior. To ensure usability and safety of use while driving, the distraction potential of system-associated tasks is most often analyzed during the development process, either by employing empirical or analytical methods, with both families of(More)
Cooperative perception makes it possible – in addition to emergency warnings – to provide drivers with early advisory warnings about potentially dangerous driving situations. Based on research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations by the(More)
We evaluated a system to support the driver in urban intersections (called "Assistance on Demand" AoD system). The system is controlled via speech and supports the driver in monitoring and decision making by providing recommendations for suitable time gaps to enter the intersection. This speech-based control of the system allows the implementation of an(More)
Conditionally Automated Driving (CAD) functions need to be carefully examined regarding related driver attitudes such as trust and usability to increase their acceptance among future system users. By adding speech output to an existing audio-visual Human-Machine Interface (HMI), the level of trust in automation was suspected to be increased due to semantic(More)
Cooperative warning systems have a great potential to prevent traffic accidents. However, because of their predictive nature, they might also go along with an increased frequency of incorrect alarms that could limit their effectiveness. To better understand the consequences associated with incorrect alarms, a driving simulator study with N=80 drivers was(More)
This study investigated driver performance during system limits of partially automated driving. Using a motion-based driving simulator, drivers encountered different situations in which a partially automated vehicle could no longer safely keep the lateral guidance. Drivers were distracted by a non-driving related task on a touch display or driving without(More)