A smart container for real-time load occupancy estimation using embedded neural inference

Remón, Diego (Universidad de Zaragoza) ; Gascón, Alberto (Universidad de Zaragoza) ; Marco, Álvaro (Universidad de Zaragoza) ; Blanco, Teresa (Universidad de Zaragoza) ; Casas, Roberto (Universidad de Zaragoza)

Resumen: The increasing demand for sustainable urban delivery solutions has driven the adoption of cargo bikes due to their environmental benefits and adaptability to congested urban environments. These operations benefit from monitoring systems capable of estimating load occupancy (volume) to support logistical decision-making. This study presents a smart-container approach for real-time occupancy estimation using two Time-of-Flight (TOF) sensors and a compact neural model deployed on an ESP32-S3 microcontroller. TOF sensors generate distance matrices of the internal cargo space, which are processed to estimate occupied volume via the normalized FreeSpace target. In two inference stress tests, the system achieves 2 = 0.929 and 0.923, with mean absolute error (MAE) = 0.044 on the normalized FreeSpace scale (0–1), equivalent to 8.1 dm3 (4.4% of container capacity). The results support the feasibility of low-cost embedded inference for operational capacity checks in cargo-bike logistics.

Idioma: Inglés

DOI: 10.1016/j.measurement.2026.120785

Año: 2026

Publicado en: MEASUREMENT 269 (2026), 120785 [22 pp.]
ISSN: 0263-2241

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-116011RB-C22
Financiación: info:eu-repo/grantAgreement/ES/DGA/T27-23R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Expresión Gráfica en Ing. (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)

Dataset asociado: A smart container for real-time load occupancy estimation using embedded neural inference (dataset) ( https://doi.org/10.5281/zenodo.1 8614817)