Connected and autonomous vehicles-based signal vehicle coupled control

基于联网和自动驾驶车辆的信号车辆耦合控制

• 批准号: 577603-2022
• 负责人: Kim, YongHoonYONG
• 金额: $ 1.82万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748965
• 关键词: Connected; autonomous; vehicles; based; signal

The urban transportation network has been less effective due to traffic congestion, and the health of its economies suffers as a result. One potential solution is to establish coordinated route/driving mechanisms. This is enabled by recent connected and autonomous vehicle (CAV) technologies, advanced onboard computing devices, and integration with road-side infrastructures. However, engineers who design such mechanisms are still lacking scientific knowledge and effective tools that can be proven as efficient and reliable for use by the public. The goal of this international collaboration is to develop innovative approaches to the coordination of CAV vehicle drivers - online route guidance and traffic signal controls. This will be done by exploiting emerging information and computing technologies equipped in connected transportation infrastructure. This approach will improve transportation system mobility, safety, and environmental sustainability without sacrificing the interests of the individual vehicles. This research will deepen our understanding of the control mechanism among vehicles on limited traffic resources. It should also reveal the impacts of the decisions of individual vehicles on traffic congestion and offer a new paradigm of real-time traffic signal control.Canadian HQPs will get an opportunity to travel to South Korea to participate in the prototype development of the CAVs environment, access the big data and develop CAV technology under real-world conditions. Students will gain hands-on and exemplary training in designing and executing traffic signal control, analyzing the data and developing algorithms using big data.

由于交通拥堵，城市交通网络的效率降低，经济的健康也因此受到影响。一个可能的解决方案是建立协调的路线/驾驶机制。这得益于最新的联网和自动驾驶汽车（CAV）技术、先进的车载计算设备以及与路边基础设施的集成。然而，设计这种机制的工程师仍然缺乏科学知识和有效的工具，这些工具可以被证明是高效可靠的，供公众使用。这项国际合作的目标是开发创新方法来协调自动驾驶汽车驾驶员——在线路线引导和交通信号控制。这将通过利用连接交通基础设施中配备的新兴信息和计算技术来实现。这种方法将提高交通系统的机动性、安全性和环境可持续性，而不会牺牲单个车辆的利益。本研究将加深我们对有限交通资源下车辆间控制机制的理解。它还应该揭示单个车辆的决策对交通拥堵的影响，并提供实时交通信号控制的新范式。加拿大hqp将有机会前往韩国参与CAV环境的原型开发，访问大数据并在现实条件下开发CAV技术。学生将在设计和执行交通信号控制、分析数据和利用大数据开发算法方面获得实际操作和示范培训。