LATIDIA · Robótica

Seguimiento de objetos múltiples basado en IMM utilizando una red neuronal de predicción de estado

arXiv:2609.13307v1 Tipo de anuncio: nuevo Resumen: El seguimiento de objetos es esencial para que los vehículos autónomos eviten obstáculos y planifiquen rutas. El radar mantiene el rendimiento de detección incluso en condiciones climáticas adversas y puede medir el relat

La noticia

arXiv:2609.13307v1 Announce Type: new Abstract: Object tracking is essential for autonomous vehicles to avoid obstacles and plan routes. Radar maintains detection performance even in adverse weather and can measure relative velocity through the Doppler effect, making it well suited for object tracking. In this paper, we propose a data-driven state PRedictor-based Interacting Multiple Model tracking method (PR-IMM) that improves nonlinear object-motion representation while preserving the stability and interpretability of physics-based motion models. The proposed method employs a transformer-based PRediction model (PR) that incorporates radar Doppler measurements to predict object displacement. The PR model is integrated into the IMM as a mode alongside the CV, CA, and CT motion models, and