CHS: Smal: AI-Human Collaboration in Autonomous Vehicles for Safety and Security

CHS：Smal：自动驾驶汽车中的人工智能与人类协作以确保安全

• 批准号: 2245055
• 负责人: Jing Chen
• 金额: $ 50万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245055&HistoricalAwards=false
• 关键词: CHS; Smal; AI; Human; Collaboration

Autonomous vehicles (AVs) are promising to increase transportation safety and security, but the state-of-the-art artificial intelligence (AI) technologies used in AVs are still not sufficient, as evident in fatal crashes involving AVs. In the foreseeable future, human inputs and interventions will still be necessary, at least as a monitor or supervisor in AVs. Monitoring to correctly detect rare but potentially deadly events in AVs requires high levels of vigilance. The required vigilance taxes human supervisors in AVs. This project aims to overcome these challenges through novel collaboration between the AI system and the human driver. This project will result in algorithms and design principles that help reduce road accidents and are broadly applicable to other related intelligent systems in critical areas such as cybersecurity, national defense, and healthcare. Moreover, this project will support multi-disciplinary training of graduate and undergraduate students across disciplines, the development of course modules that provide students interdisciplinary experience critical to shaping the regional and national workforce, and involvement of underrepresented students in STEM fields at the graduate, undergraduate, and pre-K through 12 levels. This project addresses safety-critical challenges by developing a cognizant human-in-the-loop secure AI mechanism. The project focuses on autonomous driving incorporating three thrusts: (1) Investigate how to maintain human drivers' vigilance through secondary task assignments that incorporate the level of uncertainty in the AI decisions, (2) Develop a fault-tolerant, adversary-aware AI engine that outputs uncertainty levels in its decision as a basis for requesting human inputs. (3) Develop a vigilance-based adaptive task-allocation scheme to calibrate human vigilance online based on a quantitative vigilance model constructed from human-subject data.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.

自动驾驶汽车 (AV) 有望提高交通安全和保障，但自动驾驶汽车中使用的最先进的人工智能 (AI) 技术仍然不够，这一点在涉及自动驾驶汽车的致命事故中就可见一斑。在可预见的未来，人类的输入和干预仍然是必要的，至少作为自动驾驶汽车的监控者或监管者。要正确检测自动驾驶汽车中罕见但可能致命的事件，需要保持高度警惕。 自动驾驶汽车中的人类监管人员必须保持高度警惕。该项目旨在通过人工智能系统和人类驾驶员之间的新颖协作来克服这些挑战。该项目将产生有助于减少道路事故的算法和设计原则，并广泛适用于网络安全、国防和医疗保健等关键领域的其他相关智能系统。此外，该项目将支持跨学科研究生和本科生的多学科培训，开发课程模块，为学生提供对塑造地区和国家劳动力至关重要的跨学科经验，并让研究生、本科生在 STEM 领域代表性不足的学生参与其中。 ，以及学前班至 12 个级别。 该项目通过开发一种可识别的人机交互安全人工智能机制来解决安全关键挑战。该项目专注于自动驾驶，包含三个重点：(1) 研究如何通过包含人工智能决策中的不确定性水平的辅助任务分配来保持人类驾驶员的警惕性，(2) 开发容错、对手感知的人工智能引擎它输出其决策中的不确定性水平，作为请求人类输入的基础。 (3) 开发一种基于警惕性的自适应任务分配方案，根据人类受试者数据构建的定量警惕性模型在线校准人类警惕性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持优点和更广泛的影响审查标准。

项目成果

Optimal Transmission Power and Controller Design for Networked Control Systems Under State-Dependent Markovian Channels

状态相关马尔可夫通道下网络控制系统的最优传输功率和控制器设计

• DOI: 10.1109/tac.2022.3181758
• 发表时间: 2022-10-01
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: B. Hu;T. Tamba
• 通讯作者: T. Tamba

Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign

驾驶员对自动驾驶系统中人工智能的理解：恶意停车标志的研究

• DOI: 10.1177/15553434221117001
• 发表时间: 2022-09-08
• 期刊: Journal of Cognitive Engineering and Decision Making
• 影响因子: 2
• 作者: Katherine R. Garcia;S. Mishler;Y. Xiao;Congjiao Wang;B. Hu;J. Still;Jing Chen
• 通讯作者: Jing Chen

Boring But Demanding: Using Secondary Tasks to Counter the Driver Vigilance Decrement for Partially Automated Driving

无聊但要求高：使用辅助任务来应对半自动驾驶的驾驶员警惕性下降

• DOI: 10.1177/00187208231168697
• 发表时间: 2023-05
• 期刊: Human Factors: The Journal of the Human Factors and Ergonomics Society
• 作者: Mishler, Scott;Chen, Jing
• 通讯作者: Chen, Jing