Ario
These micro-corrections are indicative of a driver’s alertness and can, as such, be used as a drivers warning system. The pattern of this steering behavior is, however, vehicle and driver dependent, and should take into account contextual information such as road geometry. In cooperation with IMOB (UHasselt) and UZA, driving simulations were carried out to collect a vast amount of reference data. Using machine learning techniques (time-series analytics, pattern detection, forecasting) real-time sensor data and historical data are combined to assess a driver’s level of alertness.
Our client:
Ario
Actionable insights
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Data analytics
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Data engineering
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Solution design
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Ideation
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