FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation
Auteur(s): Kyamakya KyandoghereNom de la revue/Journal: IEEE ACCESS
Mois: avril
Volume: 13
Numéro: DOI: 10.1109/ACCESS.2025.3562229
Année: 2025
pages: 71082 - 71100
Résumé
<p><span style="background-color:rgb(255,255,255);color:rgb(34,34,34);">Ego vehicle speed estimation is critical for autonomous driving and advanced driver-assistance systems (ADAS), but traditional methods often fail in accuracy and computational efficiency under dynamic conditions. To address these challenges, we propose FlexiNet, a novel adaptive feature synthesis network that leverages monocular camera data to perform real-time speed estimation. FlexiNet integrates five key components, the Contextual Motion Analysis Block, Adaptive Feature Transformer, Spatial Feature Extraction Module, Motion Feature Extraction Module, and Dynamic Integration Gate, to effectively extract and fuse spatial and temporal features, thereby overcoming limitations of previous approaches by mitigating noise and capturing subtle motion dynamics. Comprehensive evaluations on the KITTI and nuImages datasets demonstrate FlexiNet’s superior performance. On the nuImages dataset, our model …</span></p>