Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models | IEEE Journals & Magazine | IEEE Xplore

Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models


Abstract:

With the growing prevalence of autonomous vehicles and the integration of intelligent and connected technologies, the demand for effective and reliable vehicle speed cont...Show More

Abstract:

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.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 73, Issue: 8, August 2024)
Page(s): 12110 - 12114
Date of Publication: 12 March 2024

ISSN Information:

Funding Agency:


References

References is not available for this document.