KDI: Adaptive Sensing and Control of Large Systems Under Uncertainty with Application to Metropolitan-Area Freeways

KDI：不确定性下大型系统的自适应传感和控制及其在大都市地区高速公路中的应用

• 批准号: 9873086
• 负责人: Jitendra Malik
• 金额: $ 170万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-01 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9873086&HistoricalAwards=false
• 关键词: KDI; Adaptive; Sensing; Control; Large

9873086MalikThe purpose of this award is to investigate the computational problems in sensing and control of a prototypical metropolitan area freeway transportation system, comprising a 1,000 lane mile network spread ver 100 square miles, used daily by 1,000,000 people in 500,000 vehicles. Technical challenges in sensing, state estimation, prediction, control and incident detection are addressed. Sensing requires real-time processing and fusing of data from loops, video cameras, probe vehicles, and cellular phones. Estimation of system state parameters (e.g. travel time distributions over numerous links) requires combining current sensor data, data from the immediate past, as well as historical data while maintaining spatial continuity constraints. Multiple scale models that can predict the evolution of system state over time and space will be developed and used to calculate real-time control actions for the available "actuators" (variable messages signs, ramp meters, signals) in order to improve performance. Based on learned characteristic signatures, the same models can be used to detect and classify incidents such as injury accidents with sufficient reliability to trigger appropriate responses.The impact of this research will be to develop the core capabilities of a traffic management information system for the twenty-first century. While the current system is monitored by a variety of sensors and vast resources are dispatched to prevent system breakdown, in the absence of computational technology to estimate system state and to predict system behavior, both transportation managers and travelers make decisions as if they were blind-folded. Systems based on this research potentially offer huge benefits to both traffic managers and travelers. For traffic managers, they will provide a real-time overview of system performance and a quantitative analysis of the impact of various control options. For travelers, they can provide real-time information and advice during the planning and execution of their trips. The techniques developed in the project also potentially have application to other problems, such as air traffic control, which share many of its essential characteristics.***

9873086马利克该奖项的目的是调查的计算问题，在传感和控制的原型大都市区高速公路交通系统，包括一个1,000车道英里的网络分布在100平方英里，每天使用的100万人在50万辆汽车。 在传感，状态估计，预测，控制和事件检测的技术挑战。 传感需要实时处理和融合来自环路、摄像机、探测车和蜂窝电话的数据。 系统状态参数的估计（例如，在多个链路上的行进时间分布）需要组合当前传感器数据、来自最近的过去的数据以及历史数据，同时保持空间连续性约束。 将开发可预测系统状态随时间和空间演变的多尺度模型，并用于计算可用“致动器”（可变信息标志、坡道仪表、信号）的实时控制行动，以提高性能。 基于学习的特征签名，相同的模型可以用于检测和分类事件，如伤害事故，具有足够的可靠性，以触发适当的responses.The影响，这项研究将开发的核心能力的交通管理信息系统的21世纪。 虽然目前的系统由各种传感器监控，并调度大量资源以防止系统崩溃，但在缺乏计算技术来估计系统状态和预测系统行为的情况下，运输管理人员和旅行者都像被蒙住眼睛一样做出决策。 基于这项研究的系统可能为交通管理者和旅行者带来巨大的好处。 对于交通管理人员，他们将提供系统性能的实时概览和各种控制方案影响的定量分析。 对于旅行者来说，他们可以在计划和执行旅行期间提供实时信息和建议。 该项目中开发的技术也有可能应用于其他问题，如空中交通管制，这些问题具有许多基本特征。