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: 2007386
• 负责人: Jing Chen
• 金额: $ 50万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007386&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）有望提高运输安全性，但在AV中使用的最先进的人工智能（AI）技术仍然不够，这在涉及AV的致命碰撞中显而易见。在可预见的未来，人类的投入和干预仍然是必要的，至少作为自动驾驶汽车的监视器或监督者。监测以正确检测AV中罕见但可能致命的事件需要高度警惕。 所需的警惕性对自动驾驶汽车中的人类主管造成了负担。该项目旨在通过人工智能系统和人类驾驶员之间的新型合作来克服这些挑战。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的研究生和本科生的多学科培训，课程模块的开发，为学生提供对塑造区域和国家劳动力至关重要的跨学科经验，以及代表性不足的学生参与STEM领域的研究生，本科和学前班到12个级别。 该项目通过开发一种认知的人在环安全AI机制来解决安全关键挑战。该项目的重点是自动驾驶，包括三个方面：（1）研究如何通过二级任务分配来保持人类驾驶员的警惕性，这些任务分配将人工智能决策中的不确定性水平纳入其中，（2）开发一个容错的、具有对抗意识的人工智能引擎，该引擎在其决策中输出不确定性水平，作为请求人类输入的基础。 (3)开发一个基于警戒的自适应任务分配方案，根据从人类受试者数据构建的定量警戒模型在线校准人类警戒。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

You See What I Want You to See: Exploring Targeted Black-Box Transferability Attack for Hash-based Image Retrieval Systems

• DOI: 10.1109/cvpr46437.2021.00197
• 发表时间: 2021-06
• 期刊: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Yanru Xiao;Cong Wang

Stochastic stability analysis for Vehicular Networked Systems with State-dependent bursty fading channels: A self-triggered approach

• DOI: 10.1016/j.automatica.2020.109352
• 发表时间: 2021
• 期刊: Autom.
• 作者: Bin Hu