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

TL;DR

This paper introduces a deep reinforcement learning-based control agent for eco-driving that learns optimal traction and transmission policies to reduce fuel consumption in commercial vehicles, outperforming traditional controllers.

Contribution

It presents a novel model-free RL approach with an off-policy actor-critic architecture for eco-driving, capable of learning from experience in complex traffic scenarios.

Findings

RL agent reduces fuel consumption more effectively than baseline controllers.

The approach adapts to real-world traffic conditions in commercial vehicles.

The method demonstrates superior performance in practical driving tests.

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.