[EN] Highlights What are the main findings? The EVACH driving simulator, equipped with custom hardware and data acquisition systems, achieved precise calibration of the braking and steering controls. Comparative analysis between naturalistic tests and virtual simulations showed that the simulator reproduces autonomous driving speeds with fidelity and stability. What is the implication of the main finding? The EVACH simulator provides a robust and reliable platform for investigating driver behavior and human-machine interaction in SAE 2 and SAE 3 scenarios. Its validated calibration and reliable virtual environment enable safe, cost-effective, and versatile experimentation for future autonomous driving research projects.Highlights What are the main findings? The EVACH driving simulator, equipped with custom hardware and data acquisition systems, achieved precise calibration of the braking and steering controls. Comparative analysis between naturalistic tests and virtual simulations showed that the simulator reproduces autonomous driving speeds with fidelity and stability. What is the implication of the main finding? The EVACH simulator provides a robust and reliable platform for investigating driver behavior and human-machine interaction in SAE 2 and SAE 3 scenarios. Its validated calibration and reliable virtual environment enable safe, cost-effective, and versatile experimentation for future autonomous driving research projects.Abstract The integration of autonomous vehicles (AVs) into road transport requires robust experimental tools to analyze the human-machine interaction, particularly under conditions of system disengagement. This study presents the primary controls calibration and virtual scenario validation of the EVACH autonomous driving simulator, designed to reproduce the SAE Level 2 and Level 3 driving modes in rural road scenarios. The simulator was customized through hardware and software developments including a dedicated data acquisition system to ensure the accurate detection of braking, steering, and other critical control inputs. Calibration tests demonstrated high fidelity, with minor errors in brake and steering control measurements, consistent with values observed in production vehicles. To validate the virtual driving rural environment, comparative experiments were conducted between naturalistic road tests and simulator-based autonomous driving, where five volunteers participated in the preliminary pilot test. Results showed that average speeds in the simulation closely matched those recorded on real roads, with differences of less than 1 km/h with minimum standard deviation and confidence values. These findings confirm that the EVACH simulator provides a stable and faithful reproduction of autonomous driving conditions. The experimental platform offers valuable support for current and future research on the safe deployment of automated vehicles., This work was funded by MCIN/AEI/10.13039/501100011033/ and by FEDER A way of making Europe, with reference PID2021-127183OA-I00.

[ES] Este Trabajo Fin de Máster analiza la geometría de carreteras convencionales y la operatividad que los vehículos automatizados (VA) de nivel SAE 2 tienen en ella, aplicando la metodología a un tramo representativo de la CV-35, CV-50 y la CV-345 en la provincia de Valencia, España. El estudio combina un análisis de la normativa y la utilización de herramientas de aprendizaje automático como son las Redes Neuronales Artificiales (ANN) para identificar las condiciones geométricas que determinan la conectividad de los sistemas automatizados y proponer medidas de mejora que aumenten dicha conectividad, aumentando así tanto la seguridad como el confort de la conducción.
La investigación parte de un análisis geométrico de los elementos de la carretera (curvas, rectas y clotoides), para posteriormente entrenar la red neuronal artificial con datos reales de circulación. Mediante la utilización de la ANN se obtiene una ecuación predictiva capaz de clasificar las curvas, identificado como elemento más relevante a la hora de desconexión del sistema, en tres categorías: conectadas, desconectadas o inciertas, a partir de las variables que mostraron mayor influencia, las cuales fueron la tasa de cambio de curvatura (CCR) y el ancho de carril, parámetros que definen la exigencia geométrica sobre el control lateral del vehículo y el margen disponible para maniobrar.
Posteriormente, se aplica la ecuación predictiva únicamente a los tramos de la CV-35, donde se analizaron 122 curvas, de las cuales 21 resultaron conectadas, 62 desconectadas y 39 se situaron en la franja de incertidumbre entre los dos umbrales de decisión, lo que refleja que las carreteras actuales no están preparadas para una automatización nivel SAE2.
A partir de este diagnóstico se proponen intervenciones geométricas selectivas, las cuales consisten en el suavizado de curvas y en la aplicación de sobreanchos de carril en zonas críticas. Estas actuaciones permiten encadenar subtramos de conexión garantizada de más de un kilómetro, mejorando además el cumplimiento normativo. Como medida complementaria, se propone la implantación de señalización vertical específica que advierta al usuario de los subtramos sin conexión, anticipando la transición a conducción manual y reforzando la seguridad vial.

[EN] The integration of autonomous vehicles (AVs) into road transport requires robust ex-perimental tools to analyze human–machine interaction, particularly under conditions of system disengagement. This study presents the development and validation of the EVACH autonomous driving simulator, designed to reproduce SAE Level 2 and Level 3 driving modes in rural road scenarios. The simulator was customized through hardware and software developments, including a dedicated data acquisition system to ensure accurate detection of braking, steering, and other critical control inputs. Calibration tests demonstrated high reliability, with minor errors in brake and steering control meas-urements, consistent with values observed in production vehicles. To validate the virtual driving rural environment, comparative experiments were conducted between natu-ralistic road tests and simulator-based autonomous driving. Results showed that average speeds in simulation closely matched those recorded on real roads, with differences of less than 1 km/h and significantly lower variability. These findings confirm that the EVACH simulator provides a stable and faithful reproduction of autonomous driving conditions. The platform represents a validated and versatile tool for evaluating driver workload, takeover performance, and human–machine interaction, offering valuable support for current and future research on the safe deployment of automated vehicles.