CAREER: Learning and Leveraging Conventions in the Design of an Adaptive Haptic Shared Control for Steering a Semi-Automated Vehicle

职业：学习和利用设计用于驾驶半自动车辆的自适应触觉共享控制的惯例

• 批准号: 2238268
• 负责人: Amirhossein Ghasemi
• 金额: $ 57.13万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238268&HistoricalAwards=false
• 关键词: CAREER; Learning; Leveraging; Conventions; Design

Humans may gravitate to different strategies for resolving a conflict. However, current solutions for control transfer in semi-automated vehicles are mainly designed based on predefined rules and do not personalize the automation's strategies for resolving a conflict. As a result, these solutions face issues such as prolonged transfer time and misinterpretations or misappropriations of responsibility. A hypothesis behind the seamless human-human collaboration is that humans can adaptively form conventions. A convention is defined as shared representations that capture the interaction and can change over time. However, forming conventions in humans-robots teams is difficult because the human partner is a non-stationary agent. In this Faculty Early Career Development (CAREER) project, the plan is to design and test adaptable and convention-based control transfer strategies to enhance joint driving performance and subjective assessment of driving. To this end, two research objectives are defined for this project. The first objective focuses on learning different forms of conventions between humans and the automation system. A modular structure that separates partner-specific conventions from task-dependent representations will be created and used to learn different forms using Bayesian-based optimization approaches. Furthermore, a map from the space of conventions to outcomes in human-machine collaboration will be characterized. The second objective focuses on developing algorithms for automation systems using multi-objective Bayesian optimization so that complex interaction policies can be learned and a desirable convention between a human and an automation system can be achieved. The effectiveness of the platform will be validated through a series of case studies with human-subject participants in the loop using a haptic steering wheel driving simulator and a ground vehicle.The overarching research objective of this CAREER grant is to further enable collaborative partnerships between teams of humans and robots. Given that both humans and robots are subject to faults, the hand-off problem – how to exchange control between a human and robot— plays a critical role in ensuring the performance of a human-robot teaming. However, balancing the driver's preference and the joint task's safety in a haptic shared control may result in several possible handover strategies. While humans seamlessly resolve conflicts by co-adapting to each other, co-adaptation between humans and robots is quite challenging. This project aims to develop the principles of dynamic co-adaptation in a haptic shared control framework wherein both a human driver and an automation system collaboratively control a semi-automated ground vehicle's steering. The educational goal is to equip students and the future workforce with the technical knowledge for designing the next generation of human-machine systems through several activities, including integrating research and teaching activities and promoting STEM education for minority students and K-12 students.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.

人们可能会倾向于采取不同的策略来解决冲突。然而，当前用于半自动车辆中的控制转移的解决方案主要是基于预定义的规则来设计的，并且没有个性化用于解决冲突的自动化策略。因此，这些解决方案面临着转移时间延长以及错误解释或挪用责任等问题。人与人之间的无缝协作背后的一个假设是，人类可以自适应地形成约定。约定被定义为捕获交互并且可以随时间改变的共享表示。然而，在人类-机器人团队中形成约定是困难的，因为人类伙伴是非静止代理。在这个教师早期职业发展（CAREER）项目中，计划是设计和测试适应性和基于惯例的控制转移策略，以提高联合驾驶性能和驾驶的主观评估。为此，本项目确定了两个研究目标。第一个目标侧重于学习人类和自动化系统之间不同形式的约定。将创建一个模块化结构，将特定于合作伙伴的约定与任务相关的表示分开，并使用基于贝叶斯的优化方法来学习不同的形式。此外，将描绘从约定空间到人机协作成果的地图。第二个目标的重点是开发自动化系统的算法，使用多目标贝叶斯优化，使复杂的交互策略可以学习和人类和自动化系统之间的理想约定可以实现。该平台的有效性将通过一系列案例研究进行验证，其中人类参与者使用触觉方向盘驾驶模拟器和地面车辆。这项CAREER资助的首要研究目标是进一步实现人类和机器人团队之间的合作伙伴关系。鉴于人类和机器人都容易出现故障，切换问题-如何在人类和机器人之间交换控制-在确保人类-机器人团队的性能方面起着关键作用。然而，在触觉共享控制中平衡驾驶员的偏好和联合任务的安全性可能导致几种可能的切换策略。虽然人类可以通过相互适应来无缝地解决冲突，但人类和机器人之间的相互适应是相当具有挑战性的。该项目旨在开发触觉共享控制框架中的动态协同适应原则，其中人类驾驶员和自动化系统协同控制半自动地面车辆的转向。教育目标是通过几项活动，使学生和未来的劳动力掌握设计下一代人机系统的技术知识，包括整合研究和教学活动，促进少数民族学生和K-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的评估支持。影响审查标准。