5 papers
TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
Miguel Antunes-GarcÃa, Santiago Montiel-MarÃn, Fabio Sánchez-GarcÃa +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-GarcÃa, Santiago Montiel-MarÃn, Fabio Sánchez-GarcÃa +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…
GaussianCaR: Gaussian Splatting for Efficient Camera-Radar Fusion
Santiago Montiel-MarÃn, Miguel Antunes-GarcÃa, Fabio Sánchez-GarcÃa +3
Robust and accurate perception of dynamic objects and map elements is crucial for autonomous vehicles performing safe navigation in complex traffic scenarios. While vision-only met…
CaR1: A Multi-Modal Baseline for BEV Vehicle Segmentation via Camera-Radar Fusion
Santiago Montiel-MarÃn, Angel Llamazares, Miguel Antunes-GarcÃa +2
Camera-radar fusion offers a robust and cost-effective alternative to LiDAR-based autonomous driving systems by combining complementary sensing capabilities: cameras provide rich s…
Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction
Miguel Antunes-GarcÃa, Luis M. Bergasa, Santiago Montiel-MarÃn +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…