Ring Shockwave Dissipation


Nov 2018

In this work, inspired by the Sugiyama experiment (cite) demonstrating spontaneous formation of traffic shockwaves, and experiments demonstrating the ability of AVs to suppress shockwaves (cite), we investigated the ability of reinforcement learning to train an optimal shockwave dissipating controller.

By training on rings of randomly varied size, and using a neural net with memory, we were able to learn a controller that both was optimal and generalized outside of the training distribution.

The figure shows a diagram of the experiment, in which a single AV tries to stabilize an unstable ring.

ring-scenario
A single AV tries to stabilize an unstable ring.
Bottleneck control design
(A). Average system velocity before and after the controller is turned on. (B). Space-time diagram of the evolution of shockwaves in the system. (C). A ring control experiment done with a single autonomous vehicle (cite DAN). (D) The 2008 Sugiyama experiment [cite]
ring-scenario
Comparisons of average system velocity under different control strategies, with the theoretical optimum as a benchmark. The GRU (memory) controller both reached the optimal on the training set, shown in white, and generalized out of distribution to the densities shown in grey.
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