Complexity analysis method of human-machine interaction task in intelligent vehicle cockpit

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.