Fuel-Efficient Switching Control for Platooning Systems With Deep Reinforcement Learning

The wide appeal of fuel-efficient transport solutions is constantly increasing due to the major impact of the transportation industry on the environment. Platooning systems represent a relatively simple approach in terms of deployment toward fuel-efficient solutions. This paper addresses the reducti...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-12, Vol.24 (12), p.13989-13999
Main Authors: Goncalves, Tiago Rocha, Cunha, Rafael Fernandes, Varma, Vineeth Satheeskumar, Elayoubi, Salah Eddine
Format: Article
Language:English
Subjects:
Adaptive control
Aerodynamics
Algorithms
Atmospheric modeling
Behavioral sciences
Computer Science
Control systems
cooperative adaptive cruise control (CACC)
Cooperative control
Cruise control
Deep learning
deep reinforcement learning
Energy consumption
Energy efficiency
Fuel consumption
Fuel economy
Fuels
Platooning
Roads
Robustness (mathematics)
Safety
Switches
Switching
Threshold logic
Transportation industry
Vehicle platoons
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Summary:The wide appeal of fuel-efficient transport solutions is constantly increasing due to the major impact of the transportation industry on the environment. Platooning systems represent a relatively simple approach in terms of deployment toward fuel-efficient solutions. This paper addresses the reduction of fuel consumption in platooning systems attainable by dynamically switching between two control policies: Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC). The switching rule is dictated by a Deep Reinforcement Learning (DRL) technique to overcome unpredictable platoon disturbances and to learn appropriate transient shift times while maximizing fuel efficiency. However, due to safety and convergence issues of DRL, our algorithm establishes transition times and minimum periods of operation of ACC and CACC controllers instead of directly controlling vehicles. Numerical experiments show that the DRL agent outperforms both static ACC and CACC versions and the threshold logic control in terms of fuel efficiency while also being robust to perturbations and satisfying safety requirements.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3304977