Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2212.07594v1
• Date: Thu, 15 Dec 2022 02:52:07 GMT
• Title: Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
• Authors: Lindsey Kerbel, Beshah Ayalew, Andrej Ivanco, Keith Loiselle
• Abstract summary: In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance. It trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
• Score: 2.064612766965483
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.

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