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

研究显示，世界上交通拥堵最严重的十大城市中，有五个位于美国。交通拥堵给美国各地的交通系统带来了不必要的负担，增加了运输成本和能源足迹。车辆自动化创造了减少交通量和提高交通基础设施效率的机会。特别是，该项目旨在减少幻影交通拥堵的能源足迹，即密集的交通在没有明显原因的情况下停止，并减少拥堵中的断断续续。该研究团队的目标是通过将一小部分联网的自动驾驶车辆(CAV)插入到有司机的正常交通中，也就是所谓的载人交通，将走走停停的拥堵造成的总体能源足迹减少高达40%。这项工作将建立混合自主(Cavs和载人交通的组合)的模型，并测试这部分Cavs以受控方式畅通交通流量的能力，从而减少能源消耗。这项研究结合了数学、控制理论、机器学习和交通工程。该项目包括四所大学，并与业界和政府合作伙伴参与。该项目还将吸引学生和社区利益相关者，包括州和联邦交通机构以及CAV制造商。具体地说，使交通畅通和减少环境足迹的技术贡献包括用于稀疏控制环境的新的平均场最优控制公式，在稀疏控制环境中，只有一部分车辆是CAV并且可以控制。研究人员将开发基于深度强化学习的数据驱动控制算法，设计用于在分析方法得出显式控制器过于复杂的环境中进行控制(例如，由于多车道、坡道和人类驾驶风格的高度变化)。他们还将开发基于可满足性模凸优化的工具，以确保这些控制器的安全性和健壮性。该方法将首先使用微观模拟工具进行验证，以评估其效率和有效性。一旦在模拟中得到验证，该项目将根据系统的实时控制命令，由100名人类司机根据通过手机应用程序与目标速度和车道进行通信的控制信号，在载人车辆上对该算法进行现场测试。之后，该系统将在项目过渡到实践部分的高速公路上插入多达20辆骑兵进行测试。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.