Below we display some videos and results from our work and publications.
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.
In this work, we demonstrated the ability of a single autonomous vehicle to control the relative spacing of vehicles following behind it to create an optimal merge.
As can be seen in the videos, at low penetration rates the autonomous vehicle bunches the vehicles to avoid the intersection, whereas at high penetrations the optimal solution becomes well timed merges.
Videos representing the depicted space-time diagrams are below. Highlighted vehicles are autonomous.
On-ramp shockwave dissipation
In this work, we demonstrate the ability of shockwave dissipation, previously demonstrated on a ring to scale to an actual highway situation. Vehicles travelling on a highway are perturbed by an aggressive on-ramp merge that the autonomous vehicle needs to dissipate.
As can be seen in the videos, the autonomous vehicle learns to slow its following vehicles to either avoid the perturbation or smooth its effects.
Inspired by the rapid decrease in lanes on the San Francisco-Oakland Bay Bridge, we study a bottleneck that merges from four lanes down to two to one.
We demonstrate that the AVs are able to learn a strategy that increases the effective outflow at high inflows, and performs competitively with ramp metering.