2006 — 2010 |
Bayen, Alexandre |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Csr---Ehs: Embedded Viability Computing @ University of California-Berkeley
In the past 15 years, viability theory has enabled significant theoretical achievements in the areas of optimal control, differential games and hybrid systems reachability. The current state of the theory now enables significant breakthroughs in embedded computing as well. This project focuses on the development of novel set-valued numerical analysis schemes and their implementation on application-specific (embedded) platforms. The research addresses three key topics: (1) Higher order numerical schemes for the computation of viability sets. These provide faster convergence rates for numerical solutions of optimal control problems. (2) Formal verification algorithms on discrete maps. These enable the creation of optimal control strategies directly applicable to problems described with arrays of measured data rather than algebraic functions. (3) Set-valued optimal control algorithms. These provide globally optimal solutions for problems in which classical control techniques cannot incorporate state constraints.
A generic computational core is being developed, customized and embedded in software and hardware platforms used for two key applications in Civil and Environmental Engineering, and in Air Traffic Control. This is integrated to a hardware platform in development: an active Lagrangian sensor network (sensors mounted on active drifters which follow environmental flows). The goal of this network is to track distributed features in water (salt fronts and turbidity plumes). The driving application is the monitoring of mixing in estuarine environments. The network will be deployed in the San-Joaquin - Sacramento Delta in California. At NASA Ames, the network is being interfaced with the software FACET, with the goal of helping Air Traffic Controllers to optimize wind routing decisions in severe weather conditions.
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1 |
2008 — 2009 |
Bayen, Alexandre |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Eu-Us 08 Workshop @ University of California-Berkeley
This award supports US participation in the EU-US Workshop on Networked Information and Control Systems, June 16th, 2008, at KTH, Stockholm, Sweden. The purpose of the meeting is to bring together European and US researchers from academia and industry to discuss research challenges and emerging industrial trends for next generation networked embedded systems. The workshop considers three themes: mobility and autonomy in cyber-physical systems; robustness and security of large-scale infrastructures; and wireless sensing and control for embedded networks
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1 |
2009 — 2015 |
Bayen, Alexandre |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Lagrangian Sensing in Large Scale Cyber-Physical Infrastructure Systems @ University of California-Berkeley
This project includes the development of theory and algorithms, the design of a system architecture, and the prototyping, testing and large-scale deployment of two mobile sensing platforms. The two applications are highway traffic estimation and river flow estimation using GPS-equipped smartphones. The algorithms run online, gather mobile data and send it to a server, which uses inverse modeling to estimate the state of the system. It broadcasts information back to the smartphones and to the internet. A field operational test, called Mobile Millennium is aimed at reaching thousands of users in California. The river flow monitoring project is aimed at deploying a hundred drifters in the Sacramento Delta.
The contributions are in the field of Lagrangian sensing in large-scale cyber-physical infrastructure systems. "Lagrangian" refers to a form of sensing in which sensors measure quantities along their trajectories; "cyber-physical" refers to the flow of information and computing in a system governed by physical phenomena. The project investigates the following question: how to reconstruct the state of a distributed system from mobile sensors which cannot control their motion? The mathematical techniques used include inverse modeling, applied to partial differential equations.
This project addresses some important issues related to transportation and clean water. With the free downloadable smartphone software, commuters have real-time traffic information to increase their travel efficiency. By involving students from underrepresented minorities, the UC Berkeley Disabled Student Program, and the Berkeley High School, this research provides a valuable opportunity for students to develop their interest in engineering.
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1 |
2009 — 2012 |
Bayen, Alexandre |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cps: Medium: Collaborative Research: Physical Modeling and Software Synthesis For Self-Reconfigurable Sensors in River Environments @ University of California-Berkeley
The objective of this research is the transformation from static sensing into mobile, actuated sensing in dynamic environments, with a focus on sensing in tidally forced rivers. The approach is to develop inverse modeling techniques to sense the environment, coordination algorithms to distribute sensors spatially, and software that uses the sensed environmental data to enable these coordination algorithms to adapt to new sensed conditions.
This work relies on the concurrent sensing of the environment and actuation of those sensors based on sensed data. Sensing the environment is approached as a two-layer optimization problem. Since mobile sensors in dynamic environments may move even when not actuated, sensor coordination and actuation algorithms must maintain connectivity for the sensors while ensuring those sensors are appropriately located. The algorithms and software developed consider the time scales of the sensed environment, as well as the motion capabilities of the mobile sensors. This closes the loop from sensing of the environment to actuation of the devices that perform that sensing.
This work is addresses a challenging problem: the management of clean water resources. Tidally forced rivers are critical elements in the water supply for millions of Californians. By involving students from underrepresented groups, this research provides a valuable opportunity for students to develop an interest in engineering and to learn first hand about the role of science and engineering in addressing environmental issues.
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1 |
2012 — 2018 |
Shenker, Scott (co-PI) [⬀] Bayen, Alexandre Stoica, Ion (co-PI) [⬀] Franklin, Michael [⬀] Jordan, Michael (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Making Sense At Scale With Algorithms, Machines, and People @ University of California-Berkeley
Making Sense at Scale with Algorithms, Machines, and People University of California, Berkeley
The world is increasingly awash in data. As more and more human activities move on line, and as a growing array of connected devices become integral part of daily life, the amount and diversity of data being generated continues to explode. According to one estimate, more than a Zettabyte (one billion terabytes) of new information was created in 2010 alone, with the rate of new information increasing by roughly 60% annually. This data takes many forms: free-form tweets, text messages, blogs and documents; structured streams produced by computers, sensors and scientific instruments; and media such as images and video. Buried in this flood of data are the keys to solving huge societal problems, for improving productivity and efficiency, for creating new economic opportunities, and for unlocking new discoveries in medicine, science and the humanities. However, raw data alone is not sufficient; we can only make sense of our world by turning this data into knowledge and insight. This challenge, known as the Big Data problem, cannot be solved by the straightforward application of current data analytics technology due to the sheer volume and diversity of information. Rather, to solve it requires throwing away old preconceptions about data management and breaking down many of the traditional boundaries in and around Computer Science and related disciplines.
The Algorithms, Machines, and People (AMP) expedition at the University of California, Berkeley is addressing this challenge head-on. AMP is a collaboration of researchers with a wide range of data-related expertise, committed to working together to create a new data analytics paradigm. AMP will produce fundamental innovations in and a deep integration of three very different types of computational resources: 1. Algorithms: new machine-learning and analysis methods that can operate at large scale and can give flexible tradeoffs between timeliness, accuracy, and cost. 2. Machines: systems infrastructure that allows programmers to easily harness the power of scalable cloud and cluster computing for making sense of data. 3. People: crowdsourcing human activity and intelligence to create hybrid human/computer solutions to problems not solvable by today's automated data analysis technologies alone.
AMP research will be guided and evaluated through close collaboration with domain experts in key societal applications including: cancer genomics and personalized medicine, large-scale sensing for traffic prediction and environmental monitoring, urban planning, and network security. Advances pioneered by the project will be made widely available through the development of the Berkeley Data Analysis System (BDAS), an open source software platform that seamlessly blends Algorithm, Machine and People resources to solve big data problems.
For more information visit http://amplab.cs.berkeley.edu
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1 |
2013 — 2018 |
Song, Dawn (co-PI) [⬀] Bayen, Alexandre Tomlin, Claire (co-PI) [⬀] Sastry, S. Shankar [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cps: Frontiers: Collaborative Research: Foundations of Resilient Cyber-Physical Systems (Forces) @ University of California-Berkeley
This NSF Cyber-Physical Systems (CPS) Frontiers project "Foundations Of Resilient CybEr-physical Systems (FORCES)" focuses on the resilient design of large-scale networked CPS systems that directly interface with humans. FORCES aims to provide comprehensive tools that allow the CPS designers and operators to combine resilient control (RC) algorithms with economic incentive (EI) schemes.
Scientific Contributions The project is developing RC tools to withstand a wide-range of attacks and faults; learning and control algorithms which integrate human actions with spatio-temporal and hybrid dynamics of networked CPS systems; and model-based design to assure semantically consistent representations across all branches of the project. Operations of networked CPS systems naturally depend on the systemic social institutions and the individual deployment choices of the humans who use and operate them. The presence of incomplete and asymmetric information among these actors leads to a gap between the individually and socially optimal equilibrium resiliency levels. The project is developing EI schemes to reduce this gap. The core contributions of the FORCES team, which includes experts in control systems, game theory, and mechanism design, are the foundations for the co-design of RC and EI schemes and technological tools for implementing them.
Expected Impacts Resilient CPS infrastructure is a critical National Asset. FORCES is contributing to the development of new Science of CPS by being the first project that integrates networked control with game theoretic tools and the economic incentives of human decision makers for resilient CPS design and operation. The FORCES integrated co-design philosophy is being validated on two CPS domains: electric power distribution and consumption, and transportation networks. These design prototypes are being tested in real world scenarios. The team's research efforts are being complemented by educational offerings on resilient CPS targeted to a large and diverse audience.
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1 |
2019 — 2021 |
Shladover, Steven Bayen, Alexandre Lu, Xiao-Yun |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cps: Ttp Option: Medium: Collaborative Research: Smoothing Traffic Via Energy-Efficient Autonomous Driving (Stead) @ University of California-Berkeley
Studies show five of the top 10 most-gridlocked cities in the world are in the United States. Traffic congestion puts undue burden on transportation systems across the United States, raising transportation costs and the energy footprint. Vehicle automation creates an opportunity to reduce traffic and improve efficiency of the transportation infrastructure. In particular, this project aims to reduce the energy footprint of phantom traffic jams, where dense traffic comes to a halt for no apparent reason, and also stop-and-go-waves in congestion. The research team aims to reduce the overall energy footprint of stop-and-go congestion by up to 40% via a small portion of connected and autonomous vehicles (CAVs) inserted into normal traffic with drivers, also known as manned traffic. The work will build models of mixed autonomy (a combination of CAVs and manned traffic), and test the ability for this portion of CAVs to smooth the flow of traffic in a controlled manner, and thus reduce the energy footprint. The research combines mathematics, control theory, machine learning, and transportation engineering. The project includes four universities and engages industry and government partners. The project will also engage students and community stakeholders, including State and Federal transportation agencies and CAV manufacturers.
Specifically, the technical contributions enabling traffic smoothing and reduction in the environmental footprint include new mean-field optimal control formulations for sparse control settings where only a subset of vehicles are CAVs and can be controlled. Investigators will develop data-driven control algorithms based on deep reinforcement learning designed to enable control in settings where analytical approaches to derive explicit controllers are too complex (e.g., due to multi-lane, ramps, and high variation of human driving styles). They will also develop tools based on Satisfiability Modulo Convex optimization to enable safety and robustness of these controllers. The approach will first be validated using microsimulation tools to assess their efficiency and their validity. Once validated in simulation, the project will then field test the algorithm with manned vehicles following real-time control commands of the system, executed by 100 human drivers following control signals communicated via a phone app with target speeds and lanes. After which, the system will be tested with up to 20 CAVs inserted onto a freeway stretch in the Transition to Practice component of the project.
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.
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1 |
2022 — 2024 |
Bayen, Alexandre Sprinkle, Jonathan (co-PI) [⬀] Work, Daniel Lee, Jonathan (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cps: Ttp Option: Medium: Coordinating Actors Via Learning For Lagrangian Systems (Calls)
This project will improve the ability to build artificial intelligence algorithms for Cyber-Physical Systems (CPS) that incorporate communications technologies by developing methods of learning from simulation environments. The specific application area is connected and automated vehicles (CAV) that drive strategically to reduce stop-and-go traffic. Employing communication between vehicles can improve the efficiency of vehicle control systems to manage traffic compared to vehicles without communication. The research of this project will explore the simulation of CAVs and how we can improve their algorithms to reduce traffic congestion, with core technology developments that are applicable to homes, health, and smart and connected communities. Increasingly at the heart of CPS are artificial intelligence algorithms, which can be programmed using a simulation of how the system should operate in the real world. A major challenge is building a simulation that accurately captures the complexity of the system in question, and how it can be controlled. The project includes partners from Toyota and Nissan that support testbeds enabling the research and accelerate transition of research to practice. The project aslo includes state and local Government stakeholders / partners which will facilitate experimentation in the real-world and demonstration of traffic congestion objectives as well as potentially emission reduction. Tools, technologies, and datasets generated in this project will be shared as active resources to support access beyond the life of the project. The project brings a focus on mentorship for undergraduate researchers, in order to broaden participation in computing.
This project will develop new reinforcement learning approaches for Lagrangian control that accommodate communication and networking between actuators. A motivating domain that will be an application area of the project is CAVs. A major challenge is leveraging a small number of CAVs before those technologies realize full adoption rates. Vehicle and infrastructure communication technologies can be more useful for congestion management when feeding into a group of sparse, coordinated Lagrangian control agents. The project will use data from existing traffic sensors and testbeds to drive learning and control development. A fleet of instrumented and controllable passenger vehicles will be used for data collection and actuation. Validation experiments will be conducted using these vehicles on live roadways, and the results will be validated using a camera-based testbed that collects detailed traffic 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.
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0.948 |