基于智能交互的汽车主动响应式交互设计
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摘要
随着主动响应式交互向车内引入,汽车可以预测用户意图并主动发起功能,从而减少分心、增加灵性,提升驾驶安全性与车内用户体验。在汽车主动响应式交互的设计中,机制层面的用户意图预测模型的准确度成为影响主动响应式交互体验好坏的关键。但是对于如何提高用户预测模型的预测准确度还缺乏行之有效的手段,目前预测模型的结果不是十分准确,相关研究还比较少。智能交互是提升机器人性能的有效手段,将其引入到主动响应式交互的设计中,能通过其获取提升用户意图模型预测准确性所需的信息,帮助突破目前算法上存在的瓶颈。提出了运用智能交互提升预测准确性的路径和基于智能交互的汽车主动响应式交互设计的框架与注意点,并结合具体案例设计进行了阐释。
With the introduction to proactive HMI, the vehicle can predict users' intentions and start functioning, thereby reducing distractions, increasing spirituality, and improving driving safety and user experience. With proactive HMI, the accuracy of the use intention prediction model at the mechanism levels to become the key to affecting the quality of the proactive HMI experience. However, there is lack of effective means for improving the accuracy of the user prediction model, and there is not sufficient relevant research. Intelligent interaction is an effective method to improve the performance of robots. By introducing this technology into the design of active responsive interaction, users' intentions can be obtained to help to break for the bottlenecks of current algorithms. This paper proposes a framework of automobile active responsive interaction design based on smart interaction and using intelligent interaction to improve prediction accuracy, the key points that deserve attention are also listed, and the specific design case is illustrate.
With the introduction to proactive HMI, the vehicle can predict users' intentions and start functioning, thereby reducing distractions, increasing spirituality, and improving driving safety and user experience. With proactive HMI, the accuracy of the use intention prediction model at the mechanism levels to become the key to affecting the quality of the proactive HMI experience. However, there is lack of effective means for improving the accuracy of the user prediction model, and there is not sufficient relevant research. Intelligent interaction is an effective method to improve the performance of robots. By introducing this technology into the design of active responsive interaction, users' intentions can be obtained to help to break for the bottlenecks of current algorithms. This paper proposes a framework of automobile active responsive interaction design based on smart interaction and using intelligent interaction to improve prediction accuracy, the key points that deserve attention are also listed, and the specific design case is illustrate.
引文
[1]BO B.Responsive media:media experiences in the age of thinking machines[EB/OL].(2017-05-10)[2018-06-19].http://creativecommons.org/licenses/by-nc-sa/4.0/.
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[2]BRUSS M,PFALZGRAF A.Proactive assistance functions for HMIs through artificial intelligence[J].Atz Worldwide,2016,118(12):40-43.
[3]BADER R.Proactive recommender systems in automotive scenarios[D].München:Technische Universit?t München,2013.
[4]SENFT E,LEMAIGNAN S,BAXTER P E,et al.Leveraging human inputs in interactive machine learning for human robot interaction[C]//Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction.New York:ACM Press,2017:281-282.
[5]CAKMAK M,CHAO C,THOMAZ A L.Designing interactions for robot active learners[J].IEEE Transactions on Autonomous Mental Development,2010,2(2):108-118.
[6]BADER R,SIEGMUND O,WOERNDL W.A study on user acceptance of proactive in-vehicle recommender systems[C]//Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications.New York:ACM Press,2011:47-54.