On the design of a decision engine for connected vehicles with an application to congestion management
For connected and autonomous vehicles, this work addresses the challenge of information overload, but the results are incremental and lack concrete performance numbers.
The paper proposes a decision engine for connected vehicles to filter relevant information, and demonstrates its effectiveness in a distributed traffic management system via simulations and hardware-in-the-loop validation.
Vehicles are becoming connected entities. As a result, a likely scenario is that such entities might be literally bombarded with information from a multitude of devices. In this context, a key challenging requirement for both connected and autonomous vehicles is that they will need to become cognitive bodies, able to parse information and use only the pieces of information that are relevant to the vehicle in the context of a given journey. In order to address this fundamental requirement, a decision engine is presented in this paper. The engine makes it possible for the vehicle to understand which pieces of information are really relevant, and subsequently to process only those pieces of information. In order to illustrate the key features of our system, we show that it is possible to build upon the engine to develop a distributed traffic management system, and then we validate such a system via both conventional (numerical and SUMO-based) simulations and a Hardware-in-the-Loop (HIL) platform. Both the conventional simulations and the HIL validation showed that the engine can be effectively used to design a distributed traffic management system.