The Application of Multi-view and Multi-task Learning for On-Board Interaction Design Based on Visual Selection
The core of information visualization and visual selection is the mapping from abstract data to visual structure. The aim of information visualization doesn’t lie in visualization itself, its ultimate aim is to collect information on the basis of visualization so as to offer support to decision making. Under the complex driving environment, Designers have to continue their research during the process of interface design. They can explore the implications and presentation methods of interface interaction inside the car in order to form an on-board interaction design system based on visual selection. This can also realize information sharing between cars and X (people, cars, roads and backstage) and possess functions like strong sensation for complex environment, intelligent decision and mutual control. At the same time, on-board interaction equipment will have more diversified tasks. For example, the alternation of interaction and decision-making between multiple tasks like reality conformation, cluster display, gesture interaction, speech recognition, body sensation and eye tracking. At present, the new direction for interaction design is the analysis of multitask visual selection so as to realize secure, comfortable, energy-saving and efficient driving and finally the invention of a new generation of on-board interaction design system which can perform on human behalf. Through multi-view and multi-task learning, this paper gave an analysis of on-board interface design and concluded design scheme and suggestion with optimal user experience. By combing reasonable analysis of human intelligence and sensible interface design, this paper can provide new ways of thinking and methods for future on-board interface design.
KeywordsInformation visualization On-board interaction design Interface design User experience Machine learning
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