An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control
Purpose – This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach – In this paper, an enhanced eco-driving control strategy based on an advan...
Main Authors: | Haitao Ding, Wei Li, Nan Xu, Jianwei Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Tsinghua University Press
2022-10-01
|
Series: | Journal of Intelligent and Connected Vehicles |
Subjects: | |
Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JICV-07-2022-0030/full/pdf?title=an-enhanced-eco-driving-strategy-based-on-reinforcement-learning-for-connected-electric-vehicles-cooperative-velocity-and-lane-changing-control |
Similar Items
-
Deep Reinforcement Learning Reward Function Design for Autonomous Driving in Lane-Free Traffic
by: Athanasia Karalakou, et al.
Published: (2023-03-01) -
A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning
by: Kexuan Lv, et al.
Published: (2022-05-01) -
Emergency Vehicle Aware Lane Change Decision Model for Autonomous Vehicles Using Deep Reinforcement Learning
by: Ahmed Alzubaidi, et al.
Published: (2023-01-01) -
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
by: Ruichen Xu, et al.
Published: (2024-04-01) -
SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision
by: Yang Liu, et al.
Published: (2025-01-01)