4 papers
TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García +3
Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the error propagation inherent in tr…
BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving
Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García +3
A robust awareness of how dynamic scenes evolve is essential for Autonomous Driving systems, as they must accurately detect, track, and predict the behaviour of surrounding obstacl…
Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction
Miguel Antunes-García, Luis M. Bergasa, Santiago Montiel-Marín +3
Accurate object detection and prediction are critical to ensure the safety and efficiency of self-driving architectures. Predicting object trajectories and occupancy enables autono…
PO-GUISE+: Pose and object guided transformer token selection for efficient driver action recognition
Ricardo Pizarro, Roberto Valle, Rafael Barea +3
We address the task of identifying distracted driving by analyzing in-car videos using efficient transformers. Although transformer models have achieved outstanding performance in…