Emergent Behaviors in Mixed-Autonomy Traffic

Cathy Wu, Aboudy Kreidieh, Eugene Vinitsky, Alexandre M. Bayen
; Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:398-407, 2017.

Abstract

Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understanding the impacts of automation on transportation networks. The present article formulates and approaches the mixed-autonomy traffic control problem (where both automated and human-driven vehicles are present) using the powerful framework of deep reinforcement learning (RL). The resulting policies and emergent behaviors in mixed-autonomy traffic settings provide insight for the potential for automation of traffic through mixed fleets of automated and manned vehicles. Model-free learning methods are shown to naturally select policies and behaviors previously designed by model-driven approaches, such as stabilization and platooning, known to improve ring road efficiency and to even exceed a theoretical velocity limit. Remarkably, RL succeeds at maximizing velocity by effectively leveraging the structure of the human driving behavior to form an efficient vehicle spacing for an intersection network. We describe our results in the context of existing control theoretic results for stability analysis and mixed-autonomy analysis. This article additionally introduces state equivalence classes to improve the sample complexity for the learning methods.

Cite this Paper


BibTeX
@InProceedings{pmlr-v78-wu17a, title = {Emergent Behaviors in Mixed-Autonomy Traffic}, author = {Cathy Wu and Aboudy Kreidieh and Eugene Vinitsky and Alexandre M. Bayen}, booktitle = {Proceedings of the 1st Annual Conference on Robot Learning}, pages = {398--407}, year = {2017}, editor = {Sergey Levine and Vincent Vanhoucke and Ken Goldberg}, volume = {78}, series = {Proceedings of Machine Learning Research}, month = {13--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v78/wu17a/wu17a.pdf}, url = {http://proceedings.mlr.press/v78/wu17a.html}, abstract = {Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understanding the impacts of automation on transportation networks. The present article formulates and approaches the mixed-autonomy traffic control problem (where both automated and human-driven vehicles are present) using the powerful framework of deep reinforcement learning (RL). The resulting policies and emergent behaviors in mixed-autonomy traffic settings provide insight for the potential for automation of traffic through mixed fleets of automated and manned vehicles. Model-free learning methods are shown to naturally select policies and behaviors previously designed by model-driven approaches, such as stabilization and platooning, known to improve ring road efficiency and to even exceed a theoretical velocity limit. Remarkably, RL succeeds at maximizing velocity by effectively leveraging the structure of the human driving behavior to form an efficient vehicle spacing for an intersection network. We describe our results in the context of existing control theoretic results for stability analysis and mixed-autonomy analysis. This article additionally introduces state equivalence classes to improve the sample complexity for the learning methods.} }
Endnote
%0 Conference Paper %T Emergent Behaviors in Mixed-Autonomy Traffic %A Cathy Wu %A Aboudy Kreidieh %A Eugene Vinitsky %A Alexandre M. Bayen %B Proceedings of the 1st Annual Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2017 %E Sergey Levine %E Vincent Vanhoucke %E Ken Goldberg %F pmlr-v78-wu17a %I PMLR %J Proceedings of Machine Learning Research %P 398--407 %U http://proceedings.mlr.press %V 78 %W PMLR %X Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understanding the impacts of automation on transportation networks. The present article formulates and approaches the mixed-autonomy traffic control problem (where both automated and human-driven vehicles are present) using the powerful framework of deep reinforcement learning (RL). The resulting policies and emergent behaviors in mixed-autonomy traffic settings provide insight for the potential for automation of traffic through mixed fleets of automated and manned vehicles. Model-free learning methods are shown to naturally select policies and behaviors previously designed by model-driven approaches, such as stabilization and platooning, known to improve ring road efficiency and to even exceed a theoretical velocity limit. Remarkably, RL succeeds at maximizing velocity by effectively leveraging the structure of the human driving behavior to form an efficient vehicle spacing for an intersection network. We describe our results in the context of existing control theoretic results for stability analysis and mixed-autonomy analysis. This article additionally introduces state equivalence classes to improve the sample complexity for the learning methods.
APA
Wu, C., Kreidieh, A., Vinitsky, E. & Bayen, A.M.. (2017). Emergent Behaviors in Mixed-Autonomy Traffic. Proceedings of the 1st Annual Conference on Robot Learning, in PMLR 78:398-407

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