Decentralized Traffic Flow Optimization Through Intrinsic Motivation

TL;DR

This paper explores using intrinsic motivation, specifically empowerment, to control autonomous cars in a decentralized manner, improving traffic flow and reducing congestion without explicit coordination.

Contribution

It introduces a novel decentralized control strategy based on empowerment for autonomous cars within the Nagel-Schreckenberg traffic model.

Findings

Improved traffic flow and reduced congestion.

Significant decrease in average traffic jam duration.

Effective decentralized control using local information.

Abstract

Traffic congestion has long been an ubiquitous problem that is exacerbating with the rapid growth of megacities. In this proof-of-concept work we study intrinsic motivation, implemented via the empowerment principle, to control autonomous car behavior to improve traffic flow. In standard models of traffic dynamics, self-organized traffic jams emerge spontaneously from the individual behavior of cars, affecting traffic over long distances. Our novel car behavior strategy improves traffic flow while still being decentralized and using only locally available information without explicit coordination. Decentralization is essential for various reasons, not least to be able to absorb robustly substantial levels of uncertainty. Our scenario is based on the well-established traffic dynamics model, the Nagel-Schreckenberg cellular automaton. In a fraction of the cars in this model, we substitute the default behavior by empowerment, our intrinsic motivation-based method. This proposed model significantly improves overall traffic flow, mitigates congestion, and reduces the average traffic jam time.