Abstract: The tasks and behaviors of human-machine interaction (HMI) in the intelligent vehicle cockpit directly
affect users’ experience in the cockpit. To prevent the risk of poor interface usability for the automobile interior and
exterior designers and car UI designers, the HMI behaviors in intelligent vehicles were studied quantitatively, and the
complexity indexes of HMI tasks were summarized. Then the specific task indexes affecting the HMI complexity in
the intelligent cockpit and their weight distribution were extracted, and an entropy-based measurement method for
HMI task complexity of intelligent vehicles was proposed. Finally, the algorithm was verified by an example of an
intelligent car cockpit. The results showed that the complexity of HMI tasks in the cockpit was impacted by many
factors, such as the logical structure of HMI task, the knowledge level and cognitive quantity of HMI, and the
complexity of HMI digital interface layout in the cockpit. These factors warrant more attention from designers. The
proposed method can help designers avoid the high risk of design complexity and cost of user learning, and assist
them to intervene in advance in the design problems related to the above indicators.

Key words: intelligent vehicle cockpit, human-machine interaction, entropy method, complexity analysis, interaction
design