The main task during operating an automotive vehicle is driving. Nowadays, distractions form a potential risk of claiming the workload necessary for the driving task. Interacting with the User Interface (UI) of the vehicle can be such a distraction. Predicting the next action on the UI can help decrease the risk of distractions. To predict the next user action, knowledge about the UI interaction behavior is necessary.
In this work, a descriptive analysis is conducted on a large naturlistic dataset which has not been matched in size by other related work in the field of automotive Human Machine Interface (HMI) research. The analysis is conducted to gain insight into the interaction behavior of the drivers. This behavior is related to several driving task conditions such as occupancy, speed and drive mode.
The results of the descriptive analysis are used to implement several prediction models to predict the next user action. Among the models, the Long Short-Term Memory (LSTM)-network achieved a validation accuracy of 87%. This work shows the potential for analysing UI interaction behavior and leveraging this for prediction purposes. It could help in forming a foundation for future adaptive UI work.