S&AS: FND: COLLAB: Adaptable Vehicular Sensing and Control for Fleet-Oriented Systems in Smart Cities

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• 批准号: 1849246
• 负责人: Fei Miao
• 金额: $ 18万
• 依托单位: University of Connecticut
• 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: 2019-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849246&HistoricalAwards=false
• 关键词: S&AS; FND; COLLAB; Adaptable; Vehicular

In smart cities of the future, how to sense, understand, and manage urban-scale vehicular systems, e.g., taxis, in an autonomous fashion (with little or no human intervention) is an essential topic to improve urban mobility efficiency, such as shorter waiting time for passengers, lower cruising mileage for drivers, and higher revenues for vehicular system operators. However, the current management strategies for vehicular systems are mainly based on individual-level data knowledge, ignoring rich information from a fleet perspective. In this project, the investigators design and implement a fleet-oriented management strategy for vehicular systems, which utilizes real-time data from sensors installed in all vehicles to improve the overall performance of the vehicular system. In particular, the investigators aim to improve the taxi system performance by using onboard cameras to detect waiting passengers on streets and share this information with nearby vehicles and dispatch centers to pick up these passengers through a dispatching strategy. The research team will develop a clear understanding of how to design an adaptive autonomous vehicular sensing and dispatching strategy to improve urban mobility efficiency from a fleet-oriented perspective, with potential applications to future fully autonomous fleets. Such an understanding on vehicular sensing and dispatch will improve the quality of the every-day life such as more efficient commute for passengers, and lower energy uses for drivers, and finally improve the environment for the society by low vehicle mileage.This research develops a fleet-oriented sensing and control framework to enable seamlessly integration of historical and real-time data within a fleet for adaptive vehicular sensing, modeling, and control. Specifically, this project studies how to best use spatiotemporally-correlated contextual information (e.g., vehicular mobility, service demand, disruptive events) among vehicles. Although such correlations decay over time and distance, it is possible to enable autonomous vehicular sensing, modeling, and control adaptively based on the following research to develop novel services: (i) reconfigurable fleet-wide coordinated sensing by autonomously learning correlated vehicular interactions; (ii) models of mobility phenomena by collectively interpreting implicit data from different vehicles with a combination of deep learning, structured learning, and attribute-based learning; (iii) designs of robust dispatching strategies with uncertainty sets and receding horizon control frameworks by iteratively considering fleet-wide knowledge to improve mobility efficiency.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.

在未来的智能城市中，如何以自主的方式（几乎没有人类干预）感知，理解和管理城市规模的车辆系统，例如出租车，是提高城市流动效率的重要话题，例如较短等待乘客的时间，较低的驾驶员巡航里程以及车辆系统操作员的收入更高。但是，当前的车辆系统管理策略主要基于个人级别的数据知识，从车队的角度忽略了丰富的信息。 在这个项目中，研究人员针对车辆系统设计并实施了面向车队的管理策略，该策略利用从所有车辆中安装的传感器中的实时数据来改善车辆系统的整体性能。特别是，调查人员旨在通过使用板载摄像头来检测街道上的等待乘客，并与附近的车辆和调度中心共享此信息，以通过调度策略来接送这些乘客，并通过登机中的乘客来提高出租车系统的性能。研究团队将对如何设计自动自动驾驶汽车感应和调度策略有清晰的了解，从面向车队的角度提高城市流动效率，并潜在地应用于未来的完全自主舰队。对车辆感测和调度的这种理解将改善每天生活的质量，例如更有效的乘客通勤，以及较低的驾驶员能量用途，并最终通过低车辆里程来改善社会的环境。这项研究发展了面向车队的传感和控制框架，可以在车队中无缝整合历史和实时数据，以进行自适应的车辆感测，建模和控制。具体而言，该项目研究了如何最好地使用车辆之间的时要信息（例如车辆移动性，服务需求，破坏性事件）。尽管这种相关性随着时间和距离的衰减，但可以根据以下研究来适应自动驾驶的车辆感测，建模和控制，以开发新的服务：（i）通过自动学习相关的车辆相互作用来重新配置范围内的机队范围内的协调感测； （ii）通过与深度学习，结构化学习和基于属性的学习结合的不同车辆中的隐性数据，通过共同解释移动性现象的模型； （iii）通过不确定性集的强大调度策略设计，并通过迭代考虑范围内的知识来提高移动效率，以恢复地平线控制框架。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子优点和更广泛的优点，更广泛的知识来支持。影响审查标准。

项目成果

Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles

• DOI: 10.48550/arxiv.2203.06333
• 发表时间: 2022-03
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Songyang Han;He Wang;Sanbao Su;Yuanyuan Shi;Fei Miao

Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

• DOI: 10.1109/iros45743.2020.9341481
• 发表时间: 2020-01-01
• 期刊: 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
• 作者: He, Sihong;Pepin, Lynn;Miao, Fei
• 通讯作者: Miao, Fei

Dynamic Pricing for Autonomous Vehicle E-hailing Services Reliability and Performance Improvement

自动驾驶汽车电子叫车服务的动态定价提高可靠性和性能

• DOI: 10.1109/coase.2019.8843122
• 发表时间: 2019
• 期刊: IEEE 15th International Conference on Automation Science and Engineering (CASE
• 作者: Wang, Qixing;Miao, Fei;Wu, Jie;Niu, Yuan;Wang, Chengliang;Lownes, Nicholas E.
• 通讯作者: Lownes, Nicholas E.

Data-driven Distributionally Robust Optimization For Vehicle Balancing of Mobility-on-Demand Systems

• DOI: 10.1145/3418287
• 发表时间: 2021-01
• 期刊: ACM Transactions on Cyber-Physical Systems
• 影响因子: 2.3
• 作者: Fei Miao;Sihong He;Lynn Pepin;Shuo Han;Abdeltawab M. Hendawi;Mohamed E. Khalefa;J. Stankovic;G. Pappas

Exploiting Beneficial Information Sharing Among Autonomous Vehicles

• DOI: 10.1109/cdc40024.2019.9029438
• 发表时间: 2019-12
• 期刊: 2019 IEEE 58th Conference on Decision and Control (CDC)
• 作者: Songyang Han;Jie Fu;Fei Miao