Flow: A Modular Learning Framework for Mixed Autonomy Traffic

The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 38; no. 2; pp. 1270 - 1286
Main Authors: Wu, Cathy, Kreidieh, Abdul Rahman, Parvate, Kanaad, Vinitsky, Eugene, Bayen, Alexandre M.
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Automation technologies for smart cities
Autonomy
Control theory
Deep learning
deep learning in robotics and automation
deep reinforcement learning
Human performance
intelligent transportation systems
Lane changing
Mathematical model
Network control
Neural networks
Numerical models
Robots
System dynamics
Traffic congestion
Traffic jams
Traffic models
Transportation systems
Uncertainty
Vehicle dynamics
ISSN:1552-3098, 1941-0468
Online Access:Get full text
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Summary:The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multivehicle interactions, and the sheer variety of scenarios represented by real-world networks. To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for overcoming these challenges in a low AV-adoption regime. A modular learning framework is presented, which leverages deep RL to address complex traffic dynamics. Modules are composed to capture common traffic phenomena (stop-and-go traffic jams, lane changing, intersections). Learned control laws are found to improve upon human driving performance, in terms of system-level velocity, by up to 57% with only 4-7% adoption of AVs. Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic-surpassing all known model-based controllers to achieve near-optimal performance-and generalize to out-of-distribution traffic densities.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2021.3087314