LATIDIA · Robótica

Learn2Drive: un marco basado en una red neuronal para el control automatizado de vehículos socialmente compatible

arXiv: 2510.21736v2Tipo de anuncio: reemplazar Resumen: Este estudio presenta un nuevo marco de control para el control de crucero adaptativo (ACC) en la conducción automatizada, aprovechando las redes neuronales y las restricciones basadas en la física. Como

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arXiv:2510.21736v2 Announce Type: replace Abstract: This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics-informed constraints. As automated vehicles (AVs) adopt advanced features like ACC, transportation systems are becoming increasingly intelligent and efficient. However, existing AV control strategies primarily focus on optimizing the performance of individual vehicles or platoons, often neglecting their interactions with human-driven vehicles (HVs) and the broader impact on traffic flow. This oversight can exacerbate congestion and reduce overall system efficiency. To address this critical research gap, we propose a neural network-based, socially compliant AV control framework that incorporates social value orientation (SVO). This framework enables AVs