On the design of a decision engine for connected vehicles with an application to congestion management

TL;DR

This paper introduces a decision engine for connected vehicles that filters relevant information for autonomous decision-making, enabling effective distributed traffic management validated through simulations and hardware testing.

Contribution

It presents a novel decision engine that helps connected vehicles identify and process only relevant information for improved congestion management.

Findings

Engine effectively filters relevant information for vehicles.

Distributed traffic management system demonstrated via simulations.

Validation confirmed engine's practical applicability.

Abstract

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.