Decentralized Traffic Flow Optimization Through Intrinsic Motivation
This addresses traffic congestion in megacities through a decentralized approach, though it appears incremental as it builds on the established Nagel-Schreckenberg model.
The paper tackles traffic congestion by using intrinsic motivation (empowerment principle) to control autonomous car behavior in a decentralized traffic model, resulting in improved traffic flow, mitigated congestion, and reduced average traffic jam time.
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.