The use of infrastructure-to-vehicle communication technologies can enable improved energy efficient autonomous driving. Traditional ecological velocity planning methods have high computational burden, particularly when plug-in hybrid electric vehicles are considered. Consequently, in order to retrieve an optimal velocity profile in real time, it is necessary to rely on significant approximations.In this paper, the aforementioned issue is addressed by exploiting deep reinforcement learning in order to learn an eco-driving velocity planner for a plug-in hybrid electric vehicle within a model-free approach. Moreover, we incorporate a state-of-the-art safety controller based on model predictive control to guarantee traffic light compliance. Statistical analysis of the simulation results demonstrate that the RL controller outperforms two benchmark controllers, and it generalizes well across a variety of intersection configurations.

Ecological Velocity Planning through Signalized Intersections: A Deep Reinforcement Learning Approach

Raimondo D. M.
Supervision
;
2020

Abstract

The use of infrastructure-to-vehicle communication technologies can enable improved energy efficient autonomous driving. Traditional ecological velocity planning methods have high computational burden, particularly when plug-in hybrid electric vehicles are considered. Consequently, in order to retrieve an optimal velocity profile in real time, it is necessary to rely on significant approximations.In this paper, the aforementioned issue is addressed by exploiting deep reinforcement learning in order to learn an eco-driving velocity planner for a plug-in hybrid electric vehicle within a model-free approach. Moreover, we incorporate a state-of-the-art safety controller based on model predictive control to guarantee traffic light compliance. Statistical analysis of the simulation results demonstrate that the RL controller outperforms two benchmark controllers, and it generalizes well across a variety of intersection configurations.
978-1-7281-7447-1
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1445014
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