CAREER- Multi-Resolution Model and Context Aware Information Networking for Cooperative Vehicle Efficiency and Safety Systems

Large scale deployment of connected and automated vehicles is impeded by significant technical and scientific gaps, especially when it comes to achieving real-time and high accuracy situational awareness for cooperating vehicles. This CAREER project aims at closing these gaps through developing fundamental information networking methodologies for coordinated control of automated systems. These methodologies are based on the innovative concept of modeled knowledge propagation. The approach is to utilize the novel concepts of model communication and its derived multi-resolution networking. Model communication relies on transmission and synchronization of models (e.g., stochastic hybrid system structures and parameters) instead of raw measurements. This allows for high fidelity synchronization of dynamical models of cooperating agents over a network. The models can be multi-resolution; they are learnt and updated in real-time as the behavior of the agent evolves. We show examples of how these models are created and communicated for control of automated and connected vehicle systems. Example applications include Cooperative Adaptive Cruise Control and Collision Detection. The results show an order of magnitude improvement in communication load or application performance.