Connected and autonomous vehicles-based signal vehicle coupled control

由于交通拥堵，城市交通网络效率降低，其经济健康状况因此受到影响。一个潜在的解决方案是建立协调的路线/驾驶机制。这得益于最近的互联和自动驾驶汽车（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有效和可靠的，可供公众使用。这项国际合作的目标是开发协调CAV车辆驾驶员的创新方法-在线路线指导和交通信号控制。这将通过利用连接的交通基础设施中配备的新兴信息和计算技术来实现。这种方法将提高运输系统的机动性、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条件下车辆之间控制机制的理解。加拿大HQP将有机会前往韩国参与CAV环境的原型开发，访问大数据并在真实条件下开发CAV技术。学生将获得设计和执行交通信号控制，分析数据和使用大数据开发算法的实践和示范培训。

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.