Data-driven Understanding of User Interaction with Human Machine Interface (HMI) in the Automotive Industry
Publicerad
Författare
Typ
Examensarbete för masterexamen
Program
Modellbyggare
Tidskriftstitel
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Volymtitel
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Sammanfattning
User behavior evaluation within the automotive field has traditionally been based
on qualitative methods like interviews and surveys. However, the improved data
availability and a stronger focus on data utilization make this approach change fast
towards the data-driven evaluation since decision-making in development needs an
evidence-based approach. Numerous studies of user interaction based on quanti tative methods like data logging have been performed. Most of them are focused
on external ECU communication (CAN, LIN, Ethernet, etc.) or video recordings.
To fully understand all user interaction steps, data collected directly from the
HMI needs to be studied. Therefore, as a first step, semi-structured interviews
with HMI Engineers at Volvo GTT were conducted to determine relevant logging
points. Afterward, a user interaction logging setup was developed for an Instru ment Cluster in trucks, based on internal ECU data. Data logged includes usage
of Instrument Views, Focus Shift, Gauges and a menu application.
In designing the logging, parts of the HMI have been modelled using Discrete
Event Systems theory to identify the system behavior. Moreover, the analysis has
been made to determine the most suitable level of the software to extract wanted
data. Finally, driver card information has proven to be a beneficial way to identify
unique users within the truck industry. Logging is done in the form of DOIDs, a
type of parameters with static definitions of the data structure. However, for the
future, a more dynamic high-frequency logging setup is needed to connect logged
user data to driving conditions and system settings.
As the user interaction logging has been verified successfully, a mixed methods
approach is proposed once real customer data is available. Logged data standalone
will not explain patterns found in the data, but a more complete picture can be
given when combining quantitative and qualitative methods.
Beskrivning
Ämne/nyckelord
User interaction, Human Machine Interface, Data logging, Automotive, User behavior evaluation, Instrument Cluster, Infotainment, Trucks, Mixed methods