A study of using a reinforcement learning method to improve fuel consumption of a connected vehicle with signal phase and timing data
A Phan, HS Yoon - 2020 - sae.org
Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility
industry: the transportation and driving experience. The connected feature of the vehicle …
industry: the transportation and driving experience. The connected feature of the vehicle …
Eco-Driving Strategy Design of Connected Vehicle among Multiple Signalized Intersections Using Constraint-enforced Reinforcement Learning
Optimizing speed profiles at urban signalized intersections, commonly referred to as an eco-
driving strategy, is acknowledged as a promising approach to improving vehicle energy …
driving strategy, is acknowledged as a promising approach to improving vehicle energy …
Energy-efficient driving for adaptive traffic signal control environment via explainable reinforcement learning
Energy-efficient driving systems can effectively reduce energy consumption during vehicle
operation. Most of the existing studies focus on the driving strategies in a fixed signal timing …
operation. Most of the existing studies focus on the driving strategies in a fixed signal timing …
Safe reinforcement learning for an energy-efficient driver assistance system
Reinforcement learning (RL)-based driver assistance systems seek to improve fuel
consumption via continual improvement of powertrain control actions considering …
consumption via continual improvement of powertrain control actions considering …
Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios
H Xu, N Zhang, Z Li, Z Zhuo, Y Zhang, Y Zhang, H Ding - Sustainability, 2023 - mdpi.com
Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is
a method to improve vehicle efficiency by optimizing driving behavior without making any …
a method to improve vehicle efficiency by optimizing driving behavior without making any …
Deep reinforcement learning agent with varying actions strategy for solving the eco-approach and departure problem at signalized intersections
Eco-approach and departure is a complex control problem wherein a driver's actions are
guided over a period of time or distance so as to optimize fuel consumption. Reinforcement …
guided over a period of time or distance so as to optimize fuel consumption. Reinforcement …
A selective federated reinforcement learning strategy for autonomous driving
Currently, the complex traffic environment challenges the fast and accurate response of a
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …
Automated eco-driving in urban scenarios using deep reinforcement learning
Urban settings are challenging environments to implement eco-driving strategies for
automated vehicles. It is often assumed that sufficient information on the preceding vehicle …
automated vehicles. It is often assumed that sufficient information on the preceding vehicle …
Navigating electric vehicles along a signalized corridor via reinforcement learning: Toward adaptive eco-driving control
J Zhang, X Jiang, S Cui, C Yang… - Transportation Research …, 2022 - journals.sagepub.com
One problem associated with the operation of electric vehicles (EVs) is the limited battery,
which cannot guarantee their endurance. The increasing electricity consumption will also …
which cannot guarantee their endurance. The increasing electricity consumption will also …
Intelligent decision making in autonomous vehicles using cognition aided reinforcement learning
H Rathore, V Bhadauria - 2022 IEEE Wireless Communications …, 2022 - ieeexplore.ieee.org
As recent advances in sensing, computing, and communications expedite proliferation of
autonomous vehicles (AV), their sharing the road with human driven vehicles presents a …
autonomous vehicles (AV), their sharing the road with human driven vehicles presents a …