Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models
Journal article, 2024

With the growing prevalence of autonomous vehicles and the integration of intelligent and connected technologies, the demand for effective and reliable vehicle speed control algorithms has become increasingly critical. Traditional car-following models, which primarily focus on individual vehicle pairs, exhibit limitations in complex traffic environments. To this end, this paper proposes an enhanced state representation for the application of multi-agent reinforcement learning (MARL) in platoon-following scenarios. Specifically, the proposed representation, influenced by feature engineering techniques in time series prediction tasks, thoroughly accounts for the intricate relative relationships between different vehicles within a platoon and can offer a distinctive perspective on traffic conditions to help improve the performance of MARL models. Experimental results show that the proposed method demonstrates superior performance in platoon-following scenarios across key metrics such as the time gap, distance gap, and speed, even reducing the time gap by 63%, compared with traditional state representation methods. These enhancements represent a significant step forward in ensuring the safety, efficiency, and reliability of platoon-following models within the context of autonomous vehicles.

Feature extraction

Predictive models

multi-agent reinforcement learning

Mathematical models

Trajectory

trajectory control

feature engineering

Reinforcement learning

Time series analysis

State representation

Modeling

Author

Hongyi Lin

Tsinghua University

Cheng Lyu

Technical University of Munich

Yixu He

Changan University

Yang Liu

Tsinghua University

Kun Gao

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Xiaobo Qu

Tsinghua University

IEEE Transactions on Vehicular Technology

0018-9545 (ISSN) 1939-9359 (eISSN)

Vol. 73 8 12110-12114

Subject Categories

Transport Systems and Logistics

Vehicle Engineering

Computer Science

DOI

10.1109/TVT.2024.3373533

More information

Latest update

9/7/2024 4