Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach

06/24/2022
by   Xia Jiang, et al.
0

The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework to implement adaptive control for an electric platoon composed of CAVs and human-driven vehicles (HDVs) at a signalized intersection. Firstly, a Markov Decision Process (MDP) model is proposed to describe the decision process of the mixed platoon. Novel state representation and reward function are designed for the model to consider the behavior of the whole platoon. Secondly, in order to deal with the delayed reward, an Augmented Random Search (ARS) algorithm is proposed. The control policy learned by the agent can guide the longitudinal motion of the CAV, which serves as the leader of the platoon. Finally, a series of simulations are carried out in simulation suite SUMO. Compared with several state-of-the-art (SOTA) reinforcement learning approaches, the proposed method can obtain a higher reward. Meanwhile, the simulation results demonstrate the effectiveness of the delay reward, which is designed to outperform distributed reward mechanism Compared with normal car-following behavior, the sensitivity analysis reveals that the energy can be saved to different extends (39.27 of the optimization goal. On the premise that travel delay is not sacrificed, the proposed control method can save up to 53.64

READ FULL TEXT

page 1

page 8

page 9

research
06/24/2022

Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach

This paper proposes an eco-driving framework for electric connected vehi...
research
05/11/2020

Delay-Aware Model-Based Reinforcement Learning for Continuous Control

Action delays degrade the performance of reinforcement learning in many ...
research
10/04/2022

Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control

With the recent advances in mobile energy storage technologies, electric...
research
01/24/2020

End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning

This paper presented a deep reinforcement learning method named Double D...
research
02/24/2021

Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control

Deep deterministic policy gradient (DDPG) based car-following strategy c...
research
11/29/2022

Airfoil Shape Optimization using Deep Q-Network

The feasibility of using reinforcement learning for airfoil shape optimi...
research
09/20/2022

Adaptive and Collaborative Bathymetric Channel-Finding Approach for Multiple Autonomous Marine Vehicle

This paper reports an investigation into the problem of rapid identifica...

Please sign up or login with your details

Forgot password? Click here to reset