English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is known that autonomous cars can increase road capacities by maintaininga smaller headway through vehicle platooning. Recent works have shown thatthese capacity increases can influence vehicles' route choices in unexpectedways similar to the well-known Braess's paradox, such that the networkcongestion might increase. In this paper, we propose that in mixed-autonomynetworks, i.e., networks where roads are shared between human-driven andautonomous cars, the headway of autonomous cars can be directly controlled toinfluence vehicles' routing and reduce congestion. We argue that the headway ofautonomous cars -- and consequently the capacity of link segments -- is notjust a fixed design choice; but rather, it can be leveraged as an{infrastructure control} strategy to {dynamically} regulate capacities. Imaginethat similar to variable speed limits which regulate the maximum speed ofvehicles on a road segment, a control policy regulates the headway ofautonomous cars along each road segment. We seek to influence vehicles' routechoices by directly controlling the headway of autonomous cars to preventBraess-like unexpected outcomes and increase network efficiency. We model thedynamics of mixed-autonomy traffic networks while accounting for the vehicles'route choice dynamics. We train an RL policy that learns to regulate theheadway of autonomous cars such that the total travel time in the network isminimized. We will show empirically that our trained policy can not onlyprevent Braess-like inefficiencies but also decrease total travel time.

Learning to Influence Vehicles' Routing in Mixed-Autonomy Networks by Dynamically Controlling the Headway of Autonomous Cars