Multi-Agent Sensor Fusion Methodology Using Deep Reinforcement Learning: Vehicle Sensors to Localization
Abstract
Despite recent major advances in autonomous driving, several challenges remain. Even with modern advanced sensors and processing systems, vehicles are still unable to detect all possible obstacles present in complex urban settings and under diverse environmental conditions. Consequently, numerous studies have investigated artificial intelligence methods to improve vehicle perception capabilities. This paper presents a new methodology using a framework named CarAware, which fuses multiple types of sensor data to predict vehicle positions using Deep Reinforcement Learning (DRL). Unlike traditional DRL applications centered on control, this approach focuses on perception. As a case study, the PPO algorithm was used to train and evaluate the effectiveness of this methodology.
Fields of Study & Subjects
Autonomous Vehicle Technology and Safety
Reinforcement Learning in Robotics
Advanced Neural Network Applications
Publication details
- Journal / source
- Sensors
- Publisher
- MDPI AG
- Volume
- 26
- Issue
- 4
- Pages
- 1105–1105
- Publication date
- 8 February 2026
- Language
- EN