CAREER: Modeling, Measuring and Controlling Human Comfort in Human-Autonomous-Machine Interaction (HaMI)

职业：在人机交互 (HaMI) 中建模、测量和控制人体舒适度

• 批准号: 1845779
• 负责人: Yunyi Jia
• 金额: $ 50万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845779&HistoricalAwards=false
• 关键词: CAREER; Modeling; Measuring; Controlling; Human

The next generation of autonomous machines, including collaborative robots and autonomous vehicles, will interact closely with humans. Despite tremendous efforts to promote safety and efficiency of such machines, low user acceptance remains a critical roadblock to their use. This project addresses the problem by developing techniques to measure human comfort and to modify machine behavior to increase human comfort while interacting with autonomous machines. The resulting advances will facilitate successful deployment of next-generation collaborative robots and autonomous vehicles that humans can interact with comfortably. The theoretical, computational, experimental, and design principles devised will apply to other autonomous machines that human interact with closely, such as surgical robots, domestic robots, and autonomous aircraft. The integrated education plan of this award will empower students, workforces, and the public by engaging them in learning about and helping to design next-generation autonomous machines that interact closely with humans.The main objective of this project is to advance the underlying science of human comfort in Human--Autonomous-Machine Interaction (HaMI) and provide transformative solutions and guidelines to design autonomous machines that humans interact with comfortably. The project will advance knowledge of the factors that influence human comfort in HaMI and create new computational models that give quantitative predictions of human comfort. It will create a novel multi-modal measurement paradigm for human comfort that uses the computational models and physiological signals to accurately measure human comfort in real-time. The project will create a novel framework to optimize the behaviors of autonomous machines to improve human comfort. Finally, it will create and disseminate comfort datasets for collaborative robots and autonomous vehicles to facilitate further research. Research-integrated teaching will educate students to understand and design autonomous machines that interact closely with humans. Outreach to K-12 students will inspire youngsters to engage in STEM learning especially in autonomous machines. Integrated academic, industrial and public dissemination will create a consortium to collectively understand, work with, and accept next-generation autonomous machines that interact closely with humans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

包括协作机器人和自动驾驶车辆在内的下一代自主机器将与人类密切互动。尽管在提高这类机器的安全性和效率方面做出了巨大努力，但用户接受度较低仍然是使用它们的关键障碍。该项目通过开发测量人类舒适度的技术并修改机器行为来解决这个问题，以在与自动机器交互时增加人类的舒适度。由此产生的进步将促进下一代协作机器人和自主车辆的成功部署，人类可以舒适地与之互动。设计的理论、计算、实验和设计原则将适用于其他与人类密切互动的自主机器，如手术机器人、家用机器人和自主飞机。该奖项的综合教育计划将通过让学生、劳动力和公众了解并帮助设计与人类密切互动的下一代自主机器来增强他们的能力。该项目的主要目标是促进人-自主-机器交互(HAMI)中人类舒适性的基本科学，并提供变革性的解决方案和指导方针来设计人类舒适交互的自主机器。该项目将促进对影响哈密人舒适度因素的了解，并创建新的计算模型，给出人的舒适度的定量预测。它将创建一种新颖的多模式人体舒适度测量范式，使用计算模型和生理信号来实时准确地测量人体舒适度。该项目将创建一个新的框架来优化自主机器的行为，以提高人类的舒适度。最后，它将创建和传播协作机器人和自动驾驶车辆的舒适度数据集，以促进进一步的研究。研究型教学将教育学生理解和设计与人类密切互动的自主机器。与K-12学生的接触将激励年轻人参与STEM学习，特别是在自主机器方面。学术、工业和公共传播的综合将创建一个联盟，共同理解、合作和接受与人类密切互动的下一代自动机器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning and Comfort in Human–Robot Interaction: A Review

人机交互中的学习和舒适度：回顾

• DOI: 10.3390/app9235152
• 发表时间: 2019
• 期刊: Applied Sciences
• 作者: Wang, Weitian;Chen, Yi;Li, Rui;Jia, Yunyi
• 通讯作者: Jia, Yunyi

Study of Human Comfort in Autonomous Vehicles Using Wearable Sensors

使用可穿戴传感器的自动驾驶汽车的人体舒适度研究

• DOI: 10.1109/tits.2021.3104827
• 发表时间: 2021
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Su, Haotian;Jia, Yunyi
• 通讯作者: Jia, Yunyi

Modeling, Learning and Prediction of Longitudinal Behaviors of Human-Driven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control

使用逆模型预测控制结合内部人类决策过程对人类驾驶车辆的纵向行为进行建模、学习和预测

• DOI: 10.1109/iros40897.2019.8968292
• 发表时间: 2019
• 期刊: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Guo, Longxiang;Jia, Yunyi
• 通讯作者: Jia, Yunyi

Predictive Control of Connected Mixed Traffic under Random Communication Constraints

• DOI: 10.1109/iros45743.2020.9341139
• 发表时间: 2020-10
• 期刊: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Longxiang Guo;Yunyi Jia

Anticipative and Predictive Control of Automated Vehicles in Communication-Constrained Connected Mixed Traffic

通信受限的混合交通中自动驾驶车辆的预期和预测控制

• DOI: 10.1109/tits.2021.3067282
• 发表时间: 2021
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Guo, Longxiang;Jia, Yunyi
• 通讯作者: Jia, Yunyi