When looking forward towards Intelligent Transportation Systems (ITS), driver warning systems are an integral part of an ITS. There is a need for warning systems that can integrate the information that is currently available in the vehicles with the information about the environment in order to make more informed and accurate decisions. These warning systems should be supported by roadside infrastructures for the acquisition and processing of global/environmental information. In such systems, the roadside infrastructures need to communicate a large amount of time-sensitive data to many of the vehicles. In such a large-scale time-sensitive system, real-time information extraction (e.g. determining the risk for each vehicle) and optimal resource (e.g. bandwidth) allocation are crucial yet computationally demanding.
This project will investigate and develop gene library based real-time information extraction and resource allocation methodology that can be adaptively tuned using the concepts of Artificial Immune System (AIS). This gene library is designed to extract only the relevant information from a vehicle to determine abnormality/risk in vehicle movements at various traffic environments and to provide optimal real-time sampling rate adaptations and emergency interventions based on the information.
