Learning to influence vehicles’ routing in mixed-autonomy networks

Road networks will soon be shared between human-driven and autonomous cars, i.e., they will operate under mixed vehicle autonomy. In this thesis, we consider control strategies that maximize throughput in mixed-autonomy traffic networks. In particular, we consider two control strategies that can influence vehicles' routing in mixed-autonomy networks such that the overall network performance is improved. First, we propose that unlike human-driven cars, which can be difficult to control, we can assume a level of control over autonomous cars, which provides us with an additional control input to affect traffic networks. We propose that in mixed-autonomy networks, the headway of autonomous cars can be assigned dynamically to influence vehicles' routing and reduce congestion. We argue that in mixed-autonomy networks, the headway of autonomous cars --- and consequently the capacity of link segments --- is not just a fixed design choice; but rather, it can be leveraged as an {infrastructure control} strategy to {dynamically} regulate capacities. Second, we consider a related but different control approach. We investigate the potential of using not constant but time-varying prices or tolls for vehicles to affect the route choices of autonomous or human-driven vehicles. We demonstrate how dynamically setting prices or tolls for traversing certain network links can increase the overall throughput of the network. To achieve these, we model the dynamics of mixed-autonomy traffic networks while accounting for the vehicles' route choice dynamics. We train an RL policy that learns to regulate either the headway of autonomous cars or the price assigned to each link, such that the total travel time in the network is minimized. We will show empirically that our trained policy can not only prevent the inefficiencies that result from selfish route choices but also decrease total travel time.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Ma, Xiaoyu

Contributors dc:contributor

• Mehr, Negar

• Intelligent Transportation Systems
• Automation Technologies For Smart Cities

Statement dc:rights

• Copyright 2023 Xiaoyu Ma

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120118

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120118