6,181 research outputs found
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments
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
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads via
platooning, even when human drivers and autonomous vehicles share roads.
However, when users of a road network choose their routes selfishly, the
resulting traffic configuration may be very inefficient. Because of this, we
consider how to influence human decisions so as to decrease congestion on these
roads. We consider a network of parallel roads with two modes of
transportation: (i) human drivers who will choose the quickest route available
to them, and (ii) ride hailing service which provides an array of autonomous
vehicle ride options, each with different prices, to users. In this work, we
seek to design these prices so that when autonomous service users choose from
these options and human drivers selfishly choose their resulting routes, road
usage is maximized and transit delay is minimized. To do so, we formalize a
model of how autonomous service users make choices between routes with
different price/delay values. Developing a preference-based algorithm to learn
the preferences of the users, and using a vehicle flow model related to the
Fundamental Diagram of Traffic, we formulate a planning optimization to
maximize a social objective and demonstrate the benefit of the proposed routing
and learning scheme.Comment: Submitted to CDC 201
Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
Designing traffic-smoothing cruise controllers that can be deployed onto
autonomous vehicles is a key step towards improving traffic flow, reducing
congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass
the common issue of having to carefully fine-tune a large traffic
microsimulator by leveraging real-world trajectory data from the I-24 highway
in Tennessee, replayed in a one-lane simulation. Using standard deep
reinforcement learning methods, we train energy-reducing wave-smoothing
policies. As an input to the agent, we observe the speed and distance of only
the vehicle in front, which are local states readily available on most recent
vehicles, as well as non-local observations about the downstream state of the
traffic. We show that at a low 4% autonomous vehicle penetration rate, we
achieve significant fuel savings of over 15% on trajectories exhibiting many
stop-and-go waves. Finally, we analyze the smoothing effect of the controllers
and demonstrate robustness to adding lane-changing into the simulation as well
as the removal of downstream information.Comment: Accepted to be published as part of the 26th IEEE International
Conference on Intelligent Transportation Systems (ITSC) 2023, Bilbao, Spain,
September 24-28, 202
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