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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Veröffentlicht in:	IEEE transactions on robotics Jg. 38; H. 2; S. 1270 - 1286
Hauptverfasser:	Wu, Cathy, Kreidieh, Abdul Rahman, Parvate, Kanaad, Vinitsky, Eugene, Bayen, Alexandre M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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