In some embodiments, the systems and methods provide a virtual traffic light control function. In some such embodiments, a cloud-based traffic light control system, characterized by including sensors in road side such as sensing devices, control devices and communication devices. In some embodiments, the sensing components of RSUs are provided on the roads (e.g, intersections) for detecting road vehicle traffic, for sensing devices associated with the cloud system over a network connection, and for uploading information to the cloud system. The cloud system analyzes the sensed information and sends information to vehicles through communication devices.

In some embodiments, the systems and methods provide a traffic state estimation function. In some such embodiments, the cloud system contains a traffic state estimation and prediction algorithm. A weighted data fusion approach is applied to estimate the traffic states, the weights of the data fusion method are determined by the quality of information provided by sensors of RSU, TCC/TCU and TOC. When the sensor is unavailable, the method estimates traffic states on predictive and estimated information, guaranteeing that the system provides a reliable traffic state under transmission and/or vehicle scarcity challenges.

In some embodiments, the systems and methods provide a fleet maintenance function. In some such embodiments, the cloud system utilizes its traffic state estimation and data fusion methods to support applications of fleet maintenance such as Remote Vehicle Diagnostics, Intelligent fuel-saving driving and Intelligent charge/refuel.

In some embodiments, the IRIS contains high performance computation capability to allocate computation power to realize sensing, prediction, planning and decision making, and control, specifically, at three levels: