Collaborative Research: CPS: Medium: Sharing the World with Autonomous Systems: What Goes Wrong and How to Fix It
合作研究:CPS:媒介:与自治系统共享世界:出了什么问题以及如何修复它
基本信息
- 批准号:2211141
- 负责人:
- 金额:$ 59.99万
- 依托单位:
- 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:2022-06-15 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
As autonomous systems start to operate in open, uncontrolled environments alongside humans, safety becomes a major concern. In applications in which human-operated systems and autonomous systems are in close interaction, the heterogeneity causes different agents to exhibit different behaviors under the same situation due to the differences in how they see the world and make decisions. For example, autonomous vehicles tend to be more conservative than average human drivers, leading to instances of confusion and frustration of human drivers when encountering an autonomous vehicle. As a result, understanding the effects of inconsistencies among interacting agents on the overall system is critical for the adoption and acceptance of autonomous systems.This project aims to develop novel algorithms that will quantitatively and succinctly characterize the sources and effects of inconsistencies in perception and decision-making among the interacting agents. Based on this characterization, the project will develop efficient synthesis algorithms for autonomous systems to actively identify the inconsistencies and influence the behaviors of the other agents to improve the safety of the overall system. A key innovation lies in a rigorous approach integrating probabilistic formal methods, games on graphs, joint perception and planning, and convex optimization. While the resulting algorithms will be applicable to a wide range of cyber-physical systems, demonstrations will leverage an experimental platform in which autonomous vehicles interact with each other as well as human-controlled vehicles in mockup urban environments. The project will likewise promote outreach to industry, regulatory agencies, and the broader cyber-physical system (CPS) community through technical short courses. All educational material, demonstrations and software generated by the project will be shared publicly.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.
随着自主系统开始在开放、不受控制的环境中与人类一起运行,安全性成为一个主要问题。在人工操作系统和自治系统紧密交互的应用中,异质性导致不同的代理在相同的情况下表现出不同的行为,这是由于他们如何看待世界和做出决策的差异。例如,自动驾驶车辆往往比普通人类驾驶员更保守,导致人类驾驶员在遇到自动驾驶车辆时感到困惑和沮丧。因此,了解相互作用的代理人之间的不一致性对整个系统的影响是至关重要的采用和接受的自治systems.This项目的目的是开发新的算法,将定量和简洁地描述的来源和相互作用的代理人之间的感知和决策不一致的影响。基于这一特征,该项目将为自治系统开发高效的综合算法,以主动识别不一致性并影响其他代理的行为,从而提高整个系统的安全性。一个关键的创新在于一个严格的方法集成概率形式化方法,游戏的图形,联合感知和规划,凸优化。虽然由此产生的算法将适用于广泛的网络物理系统,但演示将利用一个实验平台,在该平台中,自动驾驶车辆彼此交互,以及在模拟城市环境中与人类控制的车辆交互。该项目还将通过短期技术课程促进与行业、监管机构和更广泛的网络物理系统(CPS)社区的外联。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
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Formal Methods for Autonomous Systems
自治系统的形式化方法
- DOI:10.1561/2600000029
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Wongpiromsarn, Tichakorn;Ghasemi, Mahsa;Cubuktepe, Murat;Bakirtzis, Georgios;Carr, Steven;Karabag, Mustafa O.;Neary, Cyrus;Gohari, Parham;Topcu, Ufuk
- 通讯作者:Topcu, Ufuk
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Tichakorn Wongpiromsarn其他文献
MLTL Multi-type (MLTLM): A Logic for Reasoning about Signals of Different Types
MLTL Multi-type (MLTLM):推理不同类型信号的逻辑
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Gokul Hariharan;Brian Kempa;Tichakorn Wongpiromsarn;Phillip H. Jones;Kristin Y. Rozier - 通讯作者:
Kristin Y. Rozier
Tichakorn Wongpiromsarn的其他文献
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{{ truncateString('Tichakorn Wongpiromsarn', 18)}}的其他基金
CAREER: Establishing correctness of learning-enabled autonomous systems with conflicting requirements
职业:建立具有冲突需求的学习型自治系统的正确性
- 批准号:
2141153 - 财政年份:2022
- 资助金额:
$ 59.99万 - 项目类别:
Continuing Grant
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