CPS: TTP Option: Medium: Collaborative Research: Smoothing Traffic via Energy-efficient Autonomous Driving (STEAD)

CPS：TTP 选项：中：协作研究：通过节能自动驾驶 (STEAD) 平滑交通

• 批准号: 1837652
• 负责人: Daniel Work
• 金额: $ 32.96万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837652&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Medium; Collaborative

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.

研究表明，世界上十大最滑行的城市中有五个在美国。交通拥堵给整个美国的运输系统带来了不适当的负担，增加了运输成本和能源足迹。车辆自动化为降低流量并提高运输基础设施效率的机会创造了机会。特别是，该项目旨在减少幻影交通堵塞的能源足迹，那里的流量无缘无故地停止，并且在充血中也停下来波浪。研究小组旨在通过一小部分连接和自动驾驶汽车（CAVS）（cavs）（插入正常的驾驶员）（也称为载人交通）中，将停车交通拥堵的总体足迹减少多达40％。这项工作将建立混合自主权的模型（骑士和载人交通的组合），并测试这部分骑士以受控方式平滑交通流的能力，从而减少能量足迹。该研究结合了数学，控制理论，机器学习和运输工程。该项目包括四个大学和参与行业和政府合作伙伴。该项目还将吸引学生和社区利益相关者，包括州和联邦运输机构以及CAV制造商。具体而言，技术贡献，使环境足迹的交通平滑和减少的技术贡献包括用于稀疏控制设置的新的均值最佳控制配方，其中只有车辆的子集是CAVS是CAVS和可以控制的。研究人员将基于旨在在设置中进行控制的深入强化学习来开发数据驱动的控制算法，在这种情况下，分析方法得出明确控制器的分析方法过于复杂（例如，由于多车道，坡道，坡道和人类驾驶方式的高变化）。他们还将基于满足性模量凸优化开发工具，以实现这些控制器的安全性和稳健性。该方法将首先使用微仿真工具进行验证，以评估其效率和有效性。一旦在模拟中进行了验证，该项目将在系统的实时控制命令后现场测试该算法，该算法是由系统的实时控制命令进行的，该命令是由100个人驾驶员执行的，该驱动程序是通过具有目标速度和车道的电话应用程序传达的控制信号后执行的。之后，该系统将经过多达20个骑士的测试，这些骑士在过渡到实践部分的过渡中插入了一条高速公路延伸。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的审查标准来评估的值得支持的。

项目成果

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

部署从轨迹数据中学习的交通平滑巡航控制器

• DOI: 10.1109/icra46639.2022.9811912
• 发表时间: 2022
• 期刊: 2022 International Conference on Robotics and Automation (ICRA
• 作者: Lichtle, Nathan;Vinitsky, Eugene;Nice, Matthew;Seibold, Benjamin;Work, Dan;Bayen, Alexandre M.
• 通讯作者: Bayen, Alexandre M.

Model-Based String Stability of Adaptive Cruise Control Systems Using Field Data

• DOI: 10.1109/tiv.2019.2955368
• 发表时间: 2019-02
• 期刊: IEEE Transactions on Intelligent Vehicles
• 影响因子: 8.2
• 作者: George Gunter;Caroline Janssen;William Barbour;Raphael E. Stern;D. Work

Fast Vehicle Turning-Movement Counting using Localization-based Tracking

• DOI: 10.1109/cvprw53098.2021.00469
• 发表时间: 2021-06
• 期刊: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• 作者: Derek Gloudemans;D. Work

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

• DOI: 10.1145/3459609.3460530
• 发表时间: 2021-04
• 期刊: Proceedings of the Workshop on Data-Driven and Intelligent Cyber-Physical Systems
• 作者: Jonathan W. Lee;George Gunter;R. Ramadan;Sulaiman Almatrudi;Paige Arnold;John Aquino;William Barbour-William

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

• DOI: 10.1109/tiv.2020.3023674
• 发表时间: 2019-11
• 期刊: IEEE Transactions on Intelligent Vehicles
• 影响因子: 8.2
• 作者: Yanbing Wang;George Gunter;Matthew Nice;M. D. Monache;D. Work