TY - GEN
T1 - Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control
AU - Lichtle, Nathan
AU - Vinitsky, Eugene
AU - Gunter, George
AU - Velu, Akash
AU - Bayen, Alexandre M.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/19
Y1 - 2021/9/19
N2 - In this work, we demonstrate optimization of fuel economy in a large, calibrated model of a portion of the Ventura Freeway using a low penetration rate of controlled autonomous vehicles. We create waves in this network using a string-unstable car-following model and introduce a ghost cell to allow waves to propagate out of the network. Using multi-agent reinforcement learning, we then design a controller that manages to partially dampen the waves and thus increase the average energy efficiency of the system, yielding a 25% fuel consumption reduction at a 10% penetration rate. Finally, we investigate the robustness properties of the designed controller. We find that the controller regulates the system to its equilibrium speed over a wide range of speeds and penetrations outside the training set, indicating generalization and robustness.
AB - In this work, we demonstrate optimization of fuel economy in a large, calibrated model of a portion of the Ventura Freeway using a low penetration rate of controlled autonomous vehicles. We create waves in this network using a string-unstable car-following model and introduce a ghost cell to allow waves to propagate out of the network. Using multi-agent reinforcement learning, we then design a controller that manages to partially dampen the waves and thus increase the average energy efficiency of the system, yielding a 25% fuel consumption reduction at a 10% penetration rate. Finally, we investigate the robustness properties of the designed controller. We find that the controller regulates the system to its equilibrium speed over a wide range of speeds and penetrations outside the training set, indicating generalization and robustness.
UR - http://www.scopus.com/inward/record.url?scp=85118470223&partnerID=8YFLogxK
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U2 - 10.1109/ITSC48978.2021.9564682
DO - 10.1109/ITSC48978.2021.9564682
M3 - Conference contribution
AN - SCOPUS:85118470223
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2068
EP - 2073
BT - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Y2 - 19 September 2021 through 22 September 2021
ER -