Yuanyang Zuo
ABSTRACT
The perception technology of intelligent cockpit promotes the development of context-based technology, and reasonable analysis and application of context components can better benefit users by applying technology to products. This study focuses on drivers in the intelligent cockpit of automobiles, analyzes driving context and user demand, and proposes cockpit context segmentation and context components under modern perception technology. From the perspectives of researchers and users, the relationship between context components and the analysis results of user research are generated through focus group and questionnaire methods. The entropy method, which reflects objective weighting, is used to determine the weight vector of the indicators. It is found that among the major components of driving contexts, the weight of vehicle status (23.51%) is the highest, while the weight of vehicle attributes (3.38%) is the lowest. This article takes the intelligent cockpit as the starting point, and provides a theoretical and technical practical basis for users' driving experience through context construction and components analysis.
- Zheng, J. (2021). Three sets of keywords for intelligent cockpits. Automotive Observation, 10, 58-61.Google Scholar
- Walch, M., Lange, K., Baumann, M., & Weber, M. (2015). Autonomous driving: investigating the feasibility of car-driver handover assistance. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 11-18). ACM.Google ScholarDigital Library
- Schieben, A., Temme, G., Köster, F., & Flemisch, F. (2011). How to interact with a highly automated vehicle. In Generic interaction design schemes and test results of a usability assessment (pp. 251-267). DLR.Google Scholar
- Feng, Y., Sun, R., Wang, H., (2021). Current development and future trends of intelligent car cabins. Automotive Practical Technology, 46(17), 201-206.Google Scholar
- Pan, F. Y. (2018). Research on the design of in-car shared music system based on requirement analysis. [Doctoral dissertation, China Academy of Art].Google Scholar
- Karim, M. M., Li, Y., Qin, R., (2021). A system of vision sensor based deep neural networks for complex driving context analysis in support of crash risk assessment and prevention. arXiv preprint arXiv:2106.10319.Google Scholar
- Halilaj, L., Dindorkar, I., Lüttin, J., (2021). A Knowledge Graph-based Approach for Situation Comprehension in Driving Contexts. In 18th Extended Semantic Web Conference (ESWC).Google Scholar
- Guo, Z., Huang, Y., Hu, X., (2021). A Survey on Deep Learning Based Approaches for Context Understanding in Autonomous Driving. Electronics, 10(4), 471.Google ScholarCross Ref
- Pine, B. J., & Gilmore, J. H. (2011). The Experience Economy. Harvard Business Press.Google Scholar
- Zhang, T., Weng, K. N., Deng, Y., Yang, M., & Zhang, Y. J. (2020). User profile modeling of niche domain users based on web browsing behavior. Systems Engineering Theory and Practice, 40(03), 641-652.Google Scholar
- Zhang, Z., Feng, X. N., & Qian, T. Y. (2020). User profile method based on multimodal fusion technology. Journal of Peking University (Natural Science Edition), 56(01), 105-111.Google Scholar
- Musto, C., Polignano, M., Semeraro, G., de Gemmis, M., & Lops, P. (2020). Myrror: a platform for holistic user modeling. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 30(3), 477-511.Google ScholarDigital Library
- Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Con, J., Haug, M., & Hussmann, H. (2018). Bringing Transparency Design into Practice. In Proceedings of the 23rd International Conference on Intelligent User Interfaces.Google ScholarDigital Library
- Exploration of automotive contextual design. [Online]. Retrieved from https://zhuanlan.zhihu.com/p/53814332.Google Scholar
- Frees, S. (2010). Context-driven interaction in immersive virtual environments. Virtual reality, 14(4), 277-290.Google Scholar
- Stickdorn, M., & Schneider, J. (2010). "This is Service Design Thinking: Basics, Tools, Cases". BIS Publishers.Google Scholar
- Krueger, R. A., & Casey, M. A. (2015). "Focus Groups: A Practical Guide for Applied Research". Sage Publications.Google Scholar
- Fowler Jr, F. J. (2013). "Survey Research Methods". Sage Publications.Google Scholar
Recommendations
Dimensions of Context Affecting User Experience in Mobile Work
INTERACT '09: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part IIUnderstanding the contextual factors affecting user experience is essential in designing and evaluating mobile systems for mobile work. The aim of this paper is to explore these contextual factors through three case studies: of safety observation at ...
User-Oriented Context Suggestion
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and PersonalizationRecommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' ...
Comments