Multiagent traffic management

TLDR

Traffic congestion reduces productivity and quality of life in cities, and recent AI advances suggest autonomous vehicle navigation could alleviate it, building on a prior reservation‑based intersection system. This paper extends the prototype to enhance real‑world implementability. We augment the system by adding turn capability, allowing acceleration within intersections, and introducing a detailed protocol that decouples vehicle behavior from intersection control, enabling limited, reliable wireless communication and policy expression. All three enhancements were fully implemented and empirically validated, demonstrating their effectiveness.

Abstract

Traffic congestion is one of the leading causes of lost productivity and decreased standard of living in urban settings. Recent advances in artificial intelligence suggest vehicle navigation by autonomous agents will be possible in the near future. In a previous paper, we proposed a reservation-based system for alleviating traffic congestion, specifically at intersections. This paper extends our prototype implementation in several ways with the aim of making it more implementable in the real world. In particular, we 1) add the ability of vehicles to turn, 2) enable them to accelerate while in the intersection, and 3) augment their interaction capabilities with a detailed protocol such that the vehicles do not need to know anything about the intersection control policy. The use of this protocol limits the interaction of the driver agent and the intersection manager to the extent that it is a reasonable approximation of reliable wireless communication. Finally, we describe how different intersection control policies can be expressed with this protocol and limited exchange of information. All three improvements are fully implemented and tested, and we present detailed empirical results validating their effectiveness.