Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
This work addresses fuel efficiency for commercial vehicles, representing an incremental improvement over existing methods.
The paper tackled the problem of reducing fuel consumption in vehicles by developing a deep reinforcement learning agent for eco-driving and transmission control, which achieved superior fuel savings compared to a baseline controller with full efficiency knowledge.
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.