关于举办加州大学洛杉矶分校马嘉琪教授学术讲座的通知
发布时间: 2021-11-30

题目:利用协同驾驶自动化改善交通运输系统的管理和运营

Using Cooperative Driving Automation to Improve Transportation Systems Management and Operations

时间:20211203日周五9001000

地点:腾讯会议 ID42265298356

腾讯直播间:https://meeting.tencent.com/l/QALGLWoVMAcS

报告人:马嘉琪(加州大学洛杉矶分校,土木与环境工程系)

马教授官方主页https://mobility-lab.seas.ucla.edu/about/

   欢迎广大师生参加!

土木与交通学院

20211130

 

报告人简介:

马嘉琪,2008年和2010年在北京交通大学分别获得学士和硕士学位,随后分别在2013年和2014年在美国弗吉尼亚大学分别获得交通工程硕士和博士学位。毕业后进入弗吉尼亚交通研究中心任职三年,于2017年加盟辛辛那提大学任助理教授且晋升为副教授。2020年进入加州大学洛杉矶分校Samueli工程学院,目前是土木与环境工程系副教授,也是该校校交通研究所副主任。马教授领导并管理了许多由美国交通部、国家科学基金会、州立交通部以及其他联邦//地方计划资助的研究项目,研究项目涵盖智能交通系统领域,如车辆公路自动化,智能交通系统(ITS),网联式汽车,共享出行、大型智能系统的建模和仿真,以及人工智能和高级计算在交通运输中的应用。马教授是IEEE Open Journal of Intelligent Transportation SystemsJournal of Intelligent Transportation Systems的副主编。同时,马教授也是交通研究委员会(TRB)车辆公路自动化常务委员会成员、TRB人工智能和高级计算应用常务委员会成员,以及美国土木工程师协会(ASCE)智能联网自动驾驶汽车影响分委会委员,IEEE ITS Smart MobilityTransportation 5.0技术委员会的联合主席。更多详情请参阅https://mobility-lab.seas.ucla.edu/about/

 

Dr. Jiaqi Ma is an Associate Professor at the UCLA Samueli School of Engineering and Associate Director of UCLA Institute of Transportation Studies. He has led and managed many research projects funded by U.S. DOT, NSF, state DOTs, and other federal/state/local programs covering areas of smart transportation systems, such as vehicle-highway automation, Intelligent Transportation Systems (ITS), connected vehicles, shared mobility, and large-scale smart system modeling and simulation, and artificial intelligence and advanced computing applications in transportation. He is an Associate Editor of the IEEE Open Journal of Intelligent Transportation Systems and Journal of Intelligent Transportation Systems. He is Member of the Transportation Research Board (TRB) Standing Committee on Vehicle-Highway Automation, Member of TRB Standing Committee on Artificial Intelligence and Advanced Computing Applications, Member of American Society of Civil Engineers (ASCE) Connected & Autonomous Vehicles Impacts Committee, Co-Chair of the IEEE ITS Society Technical Committee on Smart Mobility and Transportation 5.0.

 

报告摘要:

随着智能联网汽车技术的迅猛发展,我们现今可以更方便、更可靠地获取实时的车辆和交通信息。利用这些数据和最先进的算法,通过对不断变化的交通需求、交通模式和其他外部条件做出快速响应,可以提高交通运输系统的安全性、效率、可持续性和韧性。本讲座将重点介绍加州大学洛杉矶分校Mobility Lab利用协同自动驾驶(CDA)进行动态交通管理的最新进展。讲座将介绍一个开源项目:OpenCDA,该项目旨在提供一个模块化框架,用于在协同仿真环境中测试协同自动驾驶算法和解决方案。一个特殊的用例算法,即基于遗传模糊系统(GFS)的协同合并和编排的算法,将会被介绍并采用上述OpenCDA框架对其进行测试。讲座还将概述 CDA 实地实验,这些实验采用由Mobility Lab领导和支持的新型测试方法,以证明 CDA 在交通运输系统管理和运营中各个方面的有效性

 

With rapid advancements in connected automated vehicle technologies, real-time vehicular and traffic information can be accessed more conveniently and reliably than ever before. It is possible to leverage these data and state-of-the-art algorithms to improve transportation system safety, efficiency, sustainability, and resilience by rapidly responding to continually changing traffic demands, patterns, and other external conditions. This presentation focuses on recent developments in using cooperative driving automation (CDA) for dynamic traffic management at the UCLA Mobility Lab. The presentation will introduce an open-source project, OpenCDA, which aims at providing a modular framework for testing cooperative automated driving algorithms and solutions in a co-simulation environment. A particular use case algorithm, genetic fuzzy systems (GFS)-based cooperative merge and platooning, will be introduced and tested using the OpenCDA framework. The presentation will also provide an overview of CDA field experiments with innovative testing methods that have been led and supported by the lab to prove various aspects of CDA effectiveness in transportation systems management and operations.