CAREER: Conflicting Traffic Streams with Mixed Traffic: Modeling and Control

这项学院早期职业发展(Career)补助金支持基础研究，以了解自主和人工控制混合交通流中的交通流模式。新兴的互联自动车辆(CAV)技术在减少交通系统拥堵、提高安全性和促进更高的能源效率方面具有巨大的潜力。虽然当道路上的所有车辆都是CAV时，预期的好处是最大的，但在不久的将来，将包括混合互联汽车(CV)、互联自动化车辆和非互联(人驾驶或HV)车辆。该项目将研究混合交通流，量化其对拥堵和交通流量的影响，并制定旨在改善整个系统性能和安全的稳健策略，从而更好地了解不同类型的车辆在交通中如何相互作用。研究结果将指导道路设计、政策和未来交通系统的长期规划的发展。研究活动将与一系列教育和外联活动紧密结合，以有效促进智能和可持续交通。这些活动包括(1)开发一套将与研究和实践界共享的工具(例如，便携式驾驶模拟器和一个开源的微型模拟平台)；(2)加强现有的工程课程；(3)扩大妇女对STEM的参与；以及(4)向包括K-12学生和教师以及跨部门研究社区在内的广泛受众提供外联服务。该项目的研究目标是(I)描述在冲突交通流中HV和CAV的驾驶行为，(Ii)混合交通对交通流的集体影响，以及(Iii)研究CAV的稳健战术级控制策略，以提高系统的吞吐量、交通流稳定性和安全性。研究包括使用驾驶模拟器和现场测试的数据收集，基于收集的数据建立不同类型车辆(即Cavs、Cavs和Hv)的行为模型，以及使用基于规则和基于人工智能(AI)的控制方法设计和评估战术级控制策略。本研究将揭示在交通冲突的背景下，CAV的合作行为以及HV和CV在合作下的行为。这项研究有望为混合交通条件下的CTS提供有效的控制策略。该推广计划将为高中生和教师提供接触大学虚拟驾驶实验室以及新兴交通系统研究挑战的机会。该奖项反映了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。

This Faculty Early Career Development (CAREER) grant supports fundamental research in understanding of traffic flow patterns in mixed autonomous and human-controlled traffic streams. Emerging connected automated vehicle (CAV) technologies hold enormous potential to reduce transportation system congestion, improve safety, and facilitate higher energy efficiency. While anticipated benefits are greatest when all vehicles on the roads are CAVs, the near-term future will consist of mixed connected vehicles (CVs) connected automated vehicles and non-connected (human-driven, or HV) vehicles. This project will study mixed traffic streams, quantifying their impact on congestion and traffic flow, and develop robust strategies intended to improve performance and safety of the system as a whole, thereby enabling better understanding of how different vehicle types interact in traffic. The results will guide the development of roadway design, policies, and long-term planning for future transportation systems. The research activities will be closely integrated with a set of education and outreach activities to effectively promote smart and sustainable transportation. These activities include (i) developing a set of tools that will be shared with the research and practice communities (e.g., portable driving simulators, and an open-sourced micro-simulation platform), (ii) enhancing existing engineering curricula, (iii) broadening participation of women in STEM, and (iv) providing outreach to a broad audience, including K-12 students and teachers as well as cross-sector research communities. The research objectives of this project are to (i) characterize the driving behaviors of HVs and CAVs in conflicting traffic streams, (ii) the collective impact of mixed traffic on the traffic flow, and (iii) investigate robust tactical-level control strategies for CAVs to improve system performance with respect to throughput, traffic flow stability, and safety. The research involves data collection on using driving simulators and field tests, establishment of behavior models for different vehicle types (i.e., CAVs, CVs, and HVs) based on the collected data, and design and evaluation of tactical level control strategies using rule-based and Artificial Intelligent (AI)-based control approaches. This research will uncover the cooperative behavior of CAVs and the behavior of HVs and CVs under cooperation in the context of conflicting traffic streams. The research is expected to produce effective control strategies for CTS with mixed traffic. The outreach plan will provide high school students and teachers with exposure to the university's virtual driving lab as well as to research challenges in emerging transportation systems.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.