Yuta Uehara

Enhancing Dimensionality Reduction in Driving Behavior Learning: Integrating SENet with VAE

2025 · International Journal of Networking and Computing

This study addresses a common limitation of conventional Variational Autoencoder (VAE)based methods in dimensionality reduction for state representation learning, especially in autonomous driving, by integrating Squeeze-and-Excitation Networks (SENet) into the VAE framework.While traditional VAE approaches …