A Sensor-Fused Deep Reinforcement Learning Framework for Multi-Agent Decision-Making in Urban Driving Environments

Ethan J. Cole, David R. Thompson, Jason T. Nguyen and Benjamin A. Wright

International Journal of Engineering Advances, 2025, vol. 2, issue 1, 101-108

Abstract: Achieving robust and efficient autonomous driving in complex and dynamically changing urban traffic environments faces numerous significant challenges, especially the need to properly handle complex and time-varying interaction behaviors among multiple agents. This study innovatively proposes a sensor-integrated deep reinforcement learning framework (SIDRL), which organically combines multimodal sensor data fusion technology with multi-agent decision-making methods based on policy optimization. The system inputs include data from lidar, cameras and vehicle-to-everything (V2X), which are initially processed through a fusion perception module and subsequently fed into a decision-making network based on proximal policy optimization (PPO) for training and inference. Comprehensive evaluation experiments were conducted on the high-fidelity CARLA 0.9.15 simulation platform, and comparisons were performed with classical deep Q-network (DQN), asynchronous advantage actor-critic (A3C), as well as advanced methods such as soft actor-critic (SAC) and multi-agent proximal policy optimization (MAPPO). The experimental results clearly demonstrate that the proposed method enhances collision avoidance capability by 23.5% and decision-making efficiency by 17.2% under complex urban traffic scenarios. The research outcomes effectively confirm the critical role of multi-sensor fusion within deep reinforcement learning frameworks in improving environmental adaptability and safety for autonomous driving vehicles, providing a valuable new direction for the development of urban autonomous driving technology.

Keywords: autonomous driving; deep reinforcement learning; sensor fusion; multi-agent system; urban traffic simulation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.gbspress.com/index.php/IJEA/article/view/266/269 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbb:ijeaaa:v:2:y:2025:i:1:p:101-108

Access Statistics for this article

More articles in International Journal of Engineering Advances from George Brown Press
Bibliographic data for series maintained by Guangyi Li ().