Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

By Raphael E. Stern, Shumo Cui, M. D. Monache, Rahul Bhadani, Matt Bunting, M. Churchill, Nathaniel P. Hamilton, R'mani Haulcy, H. Pohlmann, Fangyu Wu and 4 more. arXiv.org, 2017

803 citations recorded by the source corpus.

Abstract Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

Subfields: Automotive Engineering, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/3c1f6f2ae1d37102dd8b26af58569a7669920dda

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Citations in the corpus

Benchmarks for reinforcement learning in mixed-autonomy traffic (2018, background): “For traffic control, there are a few recurring problems that are considered, including deriving controllers to minimize propagation of shockwaves in traffic [6, 7], AV assisted merging [8, 9] and energy minimization via platooning [10].”
Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, methodology): “The target velocity at any given time is then deﬁned as: Finally, the command velocity for the vehicle at time j +1 , which also ensures that the vehicle does not crash, is: The values for all parameters in the model can be found in [58] and are also provided in Table V”
Energy saving potentials of connected and automated vehicles (2018, background): “Smoother velocity transitions of the host vehicles are expected to positively influence the motion of upstream traffic, reduce the chance of a phantom jam, caused by small disturbance, [89], [90], [91] and lower fuel used by the entire queue of vehicles as experimentally shown in [92].”
Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control (2017, methodology): “The breakthrough results [4], [7] are part of a broader core set of robotics challenges concerning the deployment of multi-agent automation systems, such as ﬂeets of self-driving cars [8], [9], coordinated trafﬁc lights [10], [11], or other coordinated infrastructure.”