GOALI: Hybrid Dynamic Feedback to Design Provably Correct Driving

GOALI：混合动态反馈设计可证明正确的驾驶

• 批准号: 1161893
• 负责人: Domitilla Del Vecchio
• 金额: $ 32万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161893&HistoricalAwards=false
• 关键词: GOALI; Hybrid; Dynamic; Feedback; Design

The research objective of this award is to establish fundamental theory, develop computational techniques, and prototype real-world implementations for next generation driver-assist systems that ensure safety and fuel efficiency at traffic intersections. The research will result in algorithms for smart vehicles that cooperate with each other at intersections, merges, and stop signs to guide their drivers toward provably safe (no collisions) and fuel efficient driving choices. The research approach is to employ hybrid dynamical models (systems described by differential equations and logic) with imperfect information and solve a safety control problem for hybrid systems with unknown state. The industry partner will enable the development of prototypes of cooperative active safety systems that ensure collision-free and fuel efficient driving. Deliverables include algorithms and prototypes for cooperative active safety systems that account for fuel efficiency.If successful, the results of this research will allow the development of cost-effective and guaranteed driver-assist systems for safety and efficiency. Therefore, it has the potential of impacting society by lowering American roadway fatalities. The driver-assist systems for fuel efficiency will contribute in lowering the economic cost of vehicle driving and reduce emission, with clear benefits to the environment. This work promotes a synergy of sensing, communication, and control technologies. This along with the industry collaboration will potentially bring a substantial technological advancement to the automotive industry in the near future. Students working on this project will have the unique opportunity to work on cutting-edge technology at the industrial site collaborating with expert practitioners. The research results will be disseminated broadly through various means, including workshops at national and international meetings and more focused workshops at both the industry site and at MIT.

该奖项的研究目标是建立基础理论，开发计算技术，并为下一代驾驶员辅助系统建立原型，以确保交通路口的安全性和燃油效率。这项研究将产生智能车辆的算法，这些算法可以在十字路口、合并和停车标志上相互合作，引导驾驶员选择可证明安全（无碰撞）和省油的驾驶选择。研究方法是采用不完全信息的混合动力模型（由微分方程和逻辑描述的系统），解决状态未知的混合系统的安全控制问题。该行业合作伙伴将开发合作主动安全系统的原型，以确保无碰撞和节油驾驶。交付成果包括考虑燃油效率的合作主动安全系统的算法和原型。如果成功，这项研究的结果将允许开发具有成本效益和保证驾驶员辅助系统的安全性和效率。因此，它有可能通过降低美国的道路死亡人数来影响社会。燃油效率的驾驶员辅助系统将有助于降低车辆驾驶的经济成本，减少排放，对环境有明显的好处。这项工作促进了传感、通信和控制技术的协同作用。这与行业合作将在不久的将来为汽车行业带来实质性的技术进步。参与该项目的学生将有独特的机会在工业现场与专家从业者合作研究尖端技术。研究结果将通过各种手段广泛传播，包括在国家和国际会议上举办讲习班，以及在工业现场和麻省理工学院举办重点更突出的讲习班。