Autonomous Vehicle Localization with Deep Reinforcement Learning - Oliveira, Henrique Barros; D'Elia, Felipe Gomes de Melo; Driemeier, Larissa (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS), 2025)
Accurately estimating a vehicle's position is a key challenge in autonomous navigation, requiring real-time performance and robustness to sensor noise, outages, and unmodeled dynamics. Recent advances in deep reinforcement learning (DRL) have shown promise as a data-driven, model-free alternative for various robotics and autonomous driving tasks. This work proposes an end-to-end DRL-based pipeline for vehicle localization using only proprioceptive sensor data, including IMU, speed, and steering angle, combined with estimates from an Extended Kalman Filter. The proposed system is built around a containerized architecture that enables simplified deployment from training in the CARLA simulator to real-time execution on embedded hardware. The approach is validated both in simulation and on a 1: 10 scale physical vehicle, demonstrating stable training convergence and real-time inference capabilities on resource-constrained platforms. While the model's accuracy is not yet sufficient for closed-loop operation, the results confirm the viability of the architecture and the training approach as a foundation for further development.