Development and Evaluation Study of Intelligent Cockpit in the Age of Large Models
<|reference_start|>Development and Evaluation Study of Intelligent Cockpit in the Age of Large Models: The development of Artificial Intelligence (AI) Large Models has a great impact on the application development of automotive Intelligent cockpit. The fusion development of Intelligent Cockpit and Large Models has become a new growth point of user experience in the industry, which also creates problems for related scholars, practitioners and users in terms of their understanding and evaluation of the user experience and the capability characteristics of the Intelligent Cockpit Large Models (ICLM). This paper aims to analyse the current situation of Intelligent cockpit, large model and AI Agent, to reveal the key of application research focuses on the integration of Intelligent Cockpit and Large Models, and to put forward a necessary limitation for the subsequent development of an evaluation system for the capability of automotive ICLM and user experience. The evaluation system, P-CAFE, proposed in this paper mainly proposes five dimensions of perception, cognition, action, feedback and evolution as the first-level indicators from the domains of cognitive architecture, user experience, and capability characteristics of large models, and many second-level indicators to satisfy the current status of the application and research focuses are selected. After expert evaluation, the weights of the indicators were determined, and the indicator system of P-CAFE was established. Finally, a complete evaluation method was constructed based on Fuzzy Hierarchical Analysis. It will lay a solid foundation for the application and evaluation of the automotive ICLM, and provide a reference for the development and improvement of the future ICLM.<|reference_end|>
@article{ma2024development,
    title={Development and Evaluation Study of Intelligent Cockpit in the Age of
  Large Models},
    author={Jun Ma, Meng Wang, Jinhui Pang, Haofen Wang, Xuejing Feng, Zhipeng Hu,
  Zhenyu Yang, Mingyang Guo, Zhenming Liu, Junwei Wang, Siyi Lu, Zhiming Gou},
    journal={arXiv preprint arXiv:2409.15795},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15795},
    primaryClass={cs.HC}
}