Authors:
Alexander Stocker
and
Gerald Musser
Affiliation:
Virtual Vehicle Research, Inffeldgasse 21a, Graz, Austria
Keyword(s):
Data-Driven Services, Driver Warning Systems, in-Vehicle Hazard Alerts, Risk Modelling.
Abstract:
Data offers a strong potential for advanced, data-driven services such as in-vehicle hazard warning systems. As data spaces and ecosystems mature, access to relevant assets for these applications will grow. This paper reviews the state of driver warning and reports on a project that developed a prototypical data-driven hazard warning system to alert drivers to potential route dangers. We present its architecture and key implementation challenges, including backend event generation, frontend warning mechanisms, data availability and integration, transformation of heterogeneous inputs into actionable warnings, definition of warning logics, handling of data validity and expiration, and human factors such as modality and user acceptance. By addressing these challenges through our prototype, the paper highlights technical and systemic requirements for dependable, data-driven warning applications in the evolving mobility data ecosystem.