Collaborative Research: Smart Vehicle Platooning Built upon Real-Time Learning and Distributed Optimization

新兴的互联和自动驾驶汽车 (CAV) 技术为减少交通拥堵和提高交通效率提供了巨大潜力。然而，许多与 CAV 相关的工作都集中在单个车辆的安全上，当混合交通（CAV 和人类驾驶车辆）在道路上相互作用时，这会影响交通效率。该项目旨在研究一组 CAV 如何通过协作速度或加速度控制来响应由人类驾驶车辆、变道请求以及异常交通和网络条件引起的外源干扰。该研究将提高涉及 CAV 的未来交通系统的道路安全和交通效率。该项目将向更广泛的受众传播研究和教育成果，包括代表性不足的大学生和 K-12 学生，特别关注少数族裔学生。该项目的具体研究目标是开发在各种正常或异常交通和/或网络条件下以车辆队列为中心的最优、自适应和弹性车辆队列控制。该项目将开发（a）集成分布式优化的先进模型预测控制，以在正常交通/网络条件下实现最佳车辆队列控制； (b) 基于混合整数规划的模型预测控制，用于适应车道变更请求的最佳车辆队列控制； (c) 在异常交通和/或网络条件下集成实时学习和分布式优化的弹性车辆队列控制。 该项目将整合交通流、控制、优化、学习和分布式计算等多个领域的最新技术，并将为复杂的现实交通条件下协调和自动化的以车队为中心的交通控制建立跨学科基础。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Emerging connected and autonomous vehicle (CAV) technologies offer great potentials to reduce traffic congestion and improve traffic efficiency. However, much of the CAV related work focuses on individual vehicles's safety, which compromises traffic efficiency when mixed traffic (CAVs and human-driven vehicles) are on the road interacting with each other. This project aims to study how a group of CAVs can respond to exogenous disturbances resulting from human-driven vehicles, lane change requests and abnormal traffic and cyber conditions through cooperative speed or acceleration control. The research will improve road safety and traffic efficiency of future transportation systems involving CAVs. This project will disseminate research and education outcomes to broader audiences, including under-represented college and K-12 students with a particular focus on minority students. The specific research objectives of this project are to develop vehicle platoon centered optimal, adaptive, and resilient vehicle platooning control under various normal or abnormal traffic and/or cyber conditions. The project will develop (a) advanced model predictive control integrating distributed optimization for optimal vehicle platooning control under normal traffic/cyber conditions; (b) mixed integer programming based model predictive control for optimal vehicle platooning control adaptive to lane change requests; (c) resilient vehicle platooning control integrating real-time learning and distributed optimization under abnormal traffic and/or cyber conditions. The project will integrate the state of the art from multiple fields including traffic flows, control, optimization, learning, and distributed computation and will establish an interdisciplinary foundation for coordinated and automated vehicle platoon centered traffic control under complex real-world traffic conditions.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.