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DTSTART;TZID=America/New_York:20230717T180000
DTEND;TZID=America/New_York:20230717T193000
DTSTAMP:20260520T031546
CREATED:20230620T170709Z
LAST-MODIFIED:20230620T170709Z
UID:79435-1689616800-1689622200@c2smart.engineering.nyu.edu
SUMMARY:Transit Techies July Meetup
DESCRIPTION:Technologists who love transit.\nTransit enthusiasts who hack!\nAt Transit Techies NYC\, speakers present transit-related projects. Presenters may be household hackers\, data scientists\, researchers\, product developers\, or you! All presentations will be technical and awesome.
URL:https://c2smart.engineering.nyu.edu/event/transit-techies-july-meetup/
CATEGORIES:Connected & Autonomous Mobility
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
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DTSTART;TZID=America/New_York:20230629T120000
DTEND;TZID=America/New_York:20230629T130000
DTSTAMP:20260520T031546
CREATED:20230620T165219Z
LAST-MODIFIED:20230622T153755Z
UID:79431-1688040000-1688043600@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Autonomous Vehicle Good Citizenry Standard
DESCRIPTION:Presented by:\nSarah Kaufman\, Interim Director\, NYU Rudin Center for Transportation\nJoseph Chow\, Associate Professor\, NYU\nThursday\, June 29\, 2023: 12:00pm – 1:00pm ET | Virtual \nNew York City is moving toward a more efficient\, safer\, and sustainable future that includes autonomous vehicles for transit\, e-commerce\, and medical transport. However\, autonomy often runs on incomplete or flawed foundations: training data sets might not prepare vehicles to “see” people of color; transit shuttles may operate without safety considerations for women\, frequent targets of sexual harassment on transit; delivery pods might be sharing personal data with several third parties. Although the City will regulate vehicle safety and efficacy on the street\, autonomous mobility must be evaluated under more ambitious and holistic standards. The Responsible Autonomous Mobility (RAM) Framework aims to identify partnership in several areas.
URL:https://c2smart.engineering.nyu.edu/event/webinar-autonomous-vehicle-good-citizenry-standard/
CATEGORIES:Connected & Autonomous Mobility
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
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DTSTART;TZID=America/New_York:20221116T160000
DTEND;TZID=America/New_York:20221116T170000
DTSTAMP:20260520T031546
CREATED:20221005T141434Z
LAST-MODIFIED:20221111T225455Z
UID:77918-1668614400-1668618000@c2smart.engineering.nyu.edu
SUMMARY:Connected Vehicle Applications: Lessons Learned and Future Research & Deployment Roundtable
DESCRIPTION:The USDOT Connected Vehicle (CV) Pilot Program sought to test vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) applications to improve tranportation systems\, mobility\, and safety with real-world deployments in New York City\, Tampa\, and Wyoming. \nThis roundtable discussion will focus on these recently completed connected vehicle pilots and the lessons learned. Our panelists will feature practitioners\, decision-makers\, and researchers involved in CV deployments and leading the way for their wide-scale adoption. \nThis roundtable caps off C2SMART’s State of the Field: Connected Vehicle Applictions series\, and there will be a synthesis of prior presentations and a discussion on future directions and applications for research\, testing\, and deployment. \nWe look forward to your participation! \nPanelists: \n \nSisinnio Concas serves as Program Director at the Center for Urban Transportation Research (CUTR) and Research Associate Professor the University of South Florida (USF) College of Engineering. Dr. Concas has over 20 years of experience as a transportation economist conducting economic impact and benefit-cost analyses for public transportation\, airport and roadway projects. Dr. Concas leads CUTR’s Autonomous & Connected Mobility Evaluation (ACME) Program. ACME focuses on producing quick-response solutions to better inform practitioners and policy maker in selecting and prioritizing cost-feasible innovative transportation alternatives. He has performed numerous research projects for the U.S. Federal Transit Administration\, Federal Highway Administration\, the Florida Department of Transportation\, state and local transportation authorities. Dr. Concas leads the Performance Measurement Evaluation and Support of the Tampa CV Pilot Deployment. \n \nDr. Mohamad Talas is the Deputy Director of ITS System Engineering\, New York City Department of Transportation. He brings long standing career experience in traffic engineering and continue with over 27 years in Traffic Engineering and Operation experience in New York City Department of Transportation. He currently serves as the Director for the NYC Department of Transportation ITS project Management\, Research and Development where he supervises the Intelligent Transportation System projects and initiatives in New York City. These projects include the development and implementation of the New York City Traffic Computerization System at the Traffic Management Center modernizing and operating over 12\,000 signals and the currently deployed Active Traffic Management System in in Manhattan(Midtown In Motion) and NYC Connected Vehicle Pilot Deployment. He has earned his PhD in Transportation Planning and Engineering at NYU -Poly University\, Master degrees in Transportation\, Planning and Engineering and a Masters in Electrical Engineering from Fairleigh Dickinson University. \n \nBilly Chupp is a data analyst and engineer at the U.S. Department of Transportation’s Volpe Transportation Systems Center in Cambridge\, Massachusetts. Mr. Chupp supports a wide range of projects at the Volpe Center including cloud database and analysis system development and management for DOT’s Chief Data Officer\, artificial intelligence and machine learning development initiatives for DOT’s ITS Joint Program Office\, and air quality modeling and data analysis projects for the Federal Highway Administration. Mr. Chupp most recently served as the technical lead on Volpe’s independent safety evaluation effort for the three connected vehicle pilot programs in New York City\, Tampa\, and Wyoming\, and continues to support the ITS JPO on data documentation and strategy efforts within the connected vehicle space and beyond. \n \nDr. Karl Wunderlich holds a joint appointment at Noblis in Washington\, DC.\, serving as both is the Director of the Surface Transportation Division and the Director of the Noblis Autonomous Systems Research Center. He is a key contributor to both research and development projects and technology deployment programs sponsored by the US Department of Transportation (USDOT) and the Federal Highway Administration (FHWA). Dr. Wunderlich is an expert in the use of simulation techniques to evaluate the potential impact of emerging technologies to improve traveler mobility or system productivity – including vehicle connectivity\, autonomy\, and blockchain. He is a published author and patent-holder in orchestrated autonomy\, which leverages blockchain to create efficient and collision-free path planning among heterogenous\, unfamiliar\, and autonomous machines. Dr. Wunderlich holds a Ph.D. in Operations Research from the University of Michigan. \nDr. Kaan Ozbay is a Professor at New York University’s Tandon School of Engineering\, and Director of C2SMART Center\, a Tier 1 USDOT University Transportation Center. Dr. Ozbay served as Principal Investigator (PI) of the NYU/C2SMART team as part of the NYCDOT-led New York City Connected Vehicle Pilot\, under USDOT’s Connected Vehicle Pilot Program. He joined NYU’s Department of Civil and Urban Engineering and Center for Urban Science and Progress (CUSP) in August 2013\, and is also Global Network Professor of Civil and Urban Engineering\, NYU Abu Dhabi (NYUAD) and Global Network Professor of Engineering and Computer Science\, NYU Shanghai (NYUSH). \nModerated by: \nJingqin (Jannie) Gao completed her Ph.D. in Transportation Planning and Engineering at NYU Tandon\, where she works with C2SMART Director Kaan Ozbay. She studied Science and Technology of Optical Information and received her B.S. from Tongji University in China and her M.S in Transportation Planning and Engineering from New York University. Her research interests lie in offline and real-time simulation modeling\, big data and machine learning approach for transportation\, and transportation economics. She also worked for the New York City Department of Transportation on modeling and data analysis to support the agency’s internal planning\, technical review processes and coordinated with external agencies on regional projects since 2012. Jingqin is the former president of NYU’s joint Institute of Transportation Engineers (ITE) and The Intelligent Transportation Society of America (ITS) Student Chapter during 2018-2019\, through which she organized various company visits\, tech talks\, women in transportation events and the 2019 ITE Northeastern District Traffic Bowl.
URL:https://c2smart.engineering.nyu.edu/event/connected-vehicle-applications-lessons-learned-and-future-research-deployment-roundtable/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220328T120000
DTEND;TZID=America/New_York:20220328T170000
DTSTAMP:20260520T031546
CREATED:20220309T223338Z
LAST-MODIFIED:20220315T182808Z
UID:74589-1648468800-1648486800@c2smart.engineering.nyu.edu
SUMMARY:Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach
DESCRIPTION:The emergence of connected and autonomous vehicles (CAVs) presents increased opportunities to mitigate traffic congestion\, improve safety and reduce accidents. Professor Zhong-Ping Jiang\, and researchers Leilei Cui and Sayan Chakraborty are applying innovative reinforcement learning control methods to one challenging aspect of CAV control: lane changing in mixed traffic. The team takes a novel approach by reducing the trajectory planning and tracking problem down to the minimization of a cost function that depends on a target way-point in the lane a CAV is targeting. They’ll discuss the integration of reinforcement learning and adaptive/approximate dynamic programming methods without assuming exact knowledge of surrounding vehicles\, while avoiding the curses of dimensionality and modeling of conventional dynamic programming\, and they’ll share simulation and validation results of this promising method towards minimizing fuel consumption and improve safety of the whole traffic stream. \nPresenters \nProfessor Zhong-Ping Jiang is known for his contributions to stability and control of interconnected nonlinear systems\, and is a key contributor to the nonlinear small-gain theory. His recent research focuses on robust adaptive dynamic programming\, learning-based optimal control\, nonlinear control\, distributed control and optimization\, and their applications to computational and systems neuroscience\, connected and autonomous vehicles\, and cyber-physical systems. \nProfessor Jiang is a Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica and of the Journal of Decision and Control and has served as Senior Editor for the IEEE Control Systems Letters (L-CSS) and Systems & Control Letters\, Subject Editor\, Associate Editor and/or Guest Editor for several journals including International Journal of Robust and Nonlinear Control\, Mathematics of Control\, Signals and Systems\, IEEE Transactions on Automatic Control\, European Journal of Control\, and Science China: Information Sciences. \nLeilei Cui is a third-year PhD student at the Department of Electrical and Computer Engineering\, New York University\, under the supervision of Professor Zhong-Ping Jiang. He received a B.S. Degree in Automation from Northwestern Polytechnical University\, Xian\, China\, in 2016\, and the M.S. degree in Control Science and Engineer from Shanghai Jiao Tong University\, Shanghai\, China\, in 2019. His research interests are reinforcement learning\, adaptive dynamic programming\, control theory\, and their applications to robotics and intelligent transportation. \nSayan Chakraborty is a first year PhD candidate at the Department of Electrical and Computer Engineering\, New York University\, under the supervision of Professor Zhong-Ping Jiang. He obtained a B.Tech. degree in Electrical Engineering from National Institute of Technology\, Silchar\, India in 2017\, and an M.Tech. degree in Electrical Engineering with specialization in Systems and Control from Indian Institute of Technology Hyderabad\, India in 2021. His research interests are data-driven control\, adaptive dynamic programming\, and their application to autonomous vehicles.
URL:https://c2smart.engineering.nyu.edu/event/lane-changing-of-autonomous-vehicles-in-mixed-traffic-environments-a-reinforcement-learning-approach/
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211215T140000
DTEND;TZID=America/New_York:20211215T150000
DTSTAMP:20260520T031546
CREATED:20211203T172444Z
LAST-MODIFIED:20211203T175934Z
UID:70419-1639576800-1639580400@c2smart.engineering.nyu.edu
SUMMARY:Performance Measurement from the New York City Connected Vehicle Pilot
DESCRIPTION:The U.S. Department of Transportation (U.S. DOT) will host a webinar to share the performance measurement from the New York City (NYC) Connected Vehicle (CV) Pilot. The webinar will be held on Wednesday\, December 15\, from 2:00 PM to 3:00 PM (ET).\n\nThe NYC CV Pilot has deployed over 450 roadside units and 3\,000 aftermarket safety devices in vehicles running 13 vehicle-to-vehicle and vehicle-to-infrastructure applications focused on safety. The webinar will provide an overview of the performance evaluation of the deployed CV applications in NYC’s urban canyon environment. The webinar will also cover NYC’s plans for the next phase of the project after completion of the CV Pilot’s Phase III operations.\nBACKGROUND \nSponsored by the U.S. DOT Intelligent Transportation Systems (ITS) Joint Program Office (JPO)\, the CV Pilot Deployment Program is a national effort to enable multiple connected vehicle applications and deploy\, test\, and operationalize cutting-edge mobile and roadside technologies. These innovative technologies and applications have the potential for immediate beneficial impacts—such as saving lives\, improving personal mobility\, enhancing economic productivity\, reducing negative environmental impacts\, and transforming public agency operations.\n\nThe U.S. DOT selected three agencies as CV Pilot deployment sites: the Wyoming Department of Transportation\, the New York City Department of Transportation\, and the Tampa Hillsborough Expressway Authority. Each site prepared a comprehensive deployment concept to ensure a rapid and efficient connected vehicle capability roll-out. The sites then worked to design\, build\, and test these deployments of integrated wireless in-vehicle\, mobile device\, and roadside technologies. The NYC CV Pilot is nearing the completion of the operational phase\, where the tested system was operated and maintained in good working order over a period of 12 months and monitored on a set of key performance metrics to measure its impact.\nFor more information about the NYC CV Pilot\, please visit https://www.its.dot.gov/pilots/pilots_nycdot.htm.
URL:https://c2smart.engineering.nyu.edu/event/performance-measurement-from-the-new-york-city-connected-vehicle-pilot/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Connected & Autonomous Mobility,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T120000
DTEND;TZID=America/New_York:20211207T133000
DTSTAMP:20260520T031546
CREATED:20211201T193405Z
LAST-MODIFIED:20211201T200034Z
UID:70355-1638878400-1638883800@c2smart.engineering.nyu.edu
SUMMARY:Cooperative Perception of Smart Roadside Unit with Edge AI for Driving Assistance
DESCRIPTION:3D object detection is essential for building autonomous driving perception systems that can detect 3D objects from sensor information and safely plan movement accordingly. Stereo cameras\, light detection\, and liDAR in existing CAV systems can be heavy and expensive\, and have suffered from computing resource limitation\, resulting in unavoidable calculation errors or delays that can lead to severe consequences. To address this challenge and provide more reliable real-time localization services for CAVs\, C2SMART Center researchers Wei Sun and Chenxi Liu have developed a smart roadside unit (SRSU) with advanced computer vision technologies for driving and parking assistance. Developed by the STAR Lab at UW\, the SRSU sensor is a multi-source traffic sensing roadside unit that can transmit data through 4G/5G data plan or Long Range (LoRa) and Narrow Band Internet of Things (NB-loT) data communication protocols. Wei and Chenxi will discuss the development and impact of the smart roadside unit\, along with a Mobile Unit for Traffic Sensing (MUST) employed for data analysis and for reliable\, efficient communication with surrounding vehicles. \n  \nDr. Wei Sun is a research associate in transportation engineering in the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW). He has a Ph.D. in transportation engineering from the University of Florida (2019) and a bachelor’s degree in transportation engineering from South China University of Technology (2014). Dr. Sun’s active research fields include Intelligent Transportation Systems (ITS)\, transportation data analytics\, traffic operations and safety\, and traffic simulation and software development. Dr. Sun has worked on research projects funded by Federal Highway Administration (FHWA)\, National Cooperative Highway Research Program (NCHRP)\, Washington State Department of Transportation (WSDOT)\, Center for Safety Equity in Transportation (CSET)\, and Pacific Northwest Transportation Consortium (PacTrans). Dr. Sun serves as reviewers for the Journal of Intelligent Transportation Systems: Technology\, Planning\, and Operations\, American Society of Civil Engineers (ASCE) Journal of Transportation Engineering\, Institute of Electrical and Electronics Engineers (IEEE) International Smart Cities Conference\, and Transportation Research Record (TRR). \n  \nChenxi Liu is a Ph.D. student in the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW). He received his master’s degree in Civil Engineering from University of Washington (2020) and bachelor’s degree in Civil Engineering from Tsinghua University (2017) in Beijing\, China. He came to University of Washington\, Seattle\, WA\, US. in 2017\, he has been a Research Assistant in Smart Transportation Research and Application Lab (STARLab). His research interest includes computer vision\, deep learning\, neural network\, and smart transportation facilities. \n 
URL:https://c2smart.engineering.nyu.edu/event/cooperative-perception-of-smart-roadside-unit-with-edge-ai-for-driving-assistance/
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211116T150000
DTEND;TZID=America/New_York:20211116T153000
DTSTAMP:20260520T031546
CREATED:20211105T120943Z
LAST-MODIFIED:20211105T120943Z
UID:69656-1637074800-1637076600@c2smart.engineering.nyu.edu
SUMMARY:Roadmap to Cooperative & Automated Transportation
DESCRIPTION:The world has placed high hopes in automated vehicle (AV) technologies in revolutionizing transportation system performance\, including multiplying roadway capacity and minimizing energy consumption. However\, research conducted by Dr. Xiaopeng (Shaw) Li and colleagues has found that existing production AVs exhibit comparable or even inferior performance compared to human-driven vehicles (HDV). To bridge this gap and realize the full potential of AVs\, Dr. Li will propose a roadmap of cooperative & automated transportation\, from optimal trajectory control in ideal conditions through a cooperative control framework incorporating edge computing and machine learning under real-world constraints. This analysis of ideal conditions (e.g.\, pure AV with perfect information and control) reveals critical theoretical properties specifying feasible time-space ranges of AV movements. Combined with customized mathematical programming and control methods\, these properties lead to efficient solutions (e.g.\, in milliseconds) to real-time optimal trajectory planning problems. The solutions discussed by Dr. Li will serve as the building blocks for solving more realistic AV control problems (e.g.\, traffic mixed with human drivers\, considering different cooperation classes\, with stochasticity and errors).
URL:https://c2smart.engineering.nyu.edu/event/roadmap-to-cooperative-automated-transportation/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Connected & Autonomous Mobility,Virtual Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211109T120000
DTEND;TZID=America/New_York:20211109T130000
DTSTAMP:20260520T031546
CREATED:20211105T150532Z
LAST-MODIFIED:20211105T160443Z
UID:69665-1636459200-1636462800@c2smart.engineering.nyu.edu
SUMMARY:Using AI to Improve CAV Operations in Mixed Traffic
DESCRIPTION:Rapid advances in artificial intelligence and machine learning (AI/ML) offer unprecedented opportunities for improving the operations of connected and autonomous vehicles (CAVs) in a traffic stream that also includes human driven vehicles. Dr. Sikai (Sky) Chen will discuss recent developments in vehicle automation with mixed traffic stream\, including the challenges and opportunities associated with AI/ML algorithm development and application for CAV operations. These include leveraging real-time data using AI/ML to improve safety\, mobility\, and efficiency\, and rapidness of response to changing traffic environments. Dr. Chen will also discuss AI/ML cooperative control algorithms for multi-agent systems that consider dynamic interactions between heterogeneous system users (e.g.\, human drivers\, connected and/or automated vehicles). Results from extensive simulation experiments will be presented to demonstrate the effectiveness of such cooperative control innovations. Insights from this research can provide guidance to CAV manufacturers and transport agencies regarding infrastructure investments specifically for CAV operations.
URL:https://c2smart.engineering.nyu.edu/event/using-ai-to-improve-cav-operations-in-mixed-traffic/
CATEGORIES:Connected & Autonomous Mobility,Virtual Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
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