2 citations · 2 across the 3 of their papers we have counts for
4 papers
Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps
Rabbia Asghar, Lukas Rummelhard, Wenqian Liu +2
Accurate prediction of driving scene is a challenging task due to uncertainty in sensor data, the complex behaviors of agents, and the possibility of multiple feasible futures. Exi…
BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving
Manuel Alejandro Diaz-Zapata, Wenqian Liu, Robin Baruffa +1
Current research in semantic bird's-eye view segmentation for autonomous driving focuses solely on optimizing neural network models using a single dataset, typically nuScenes. This…
Flow-guided Motion Prediction with Semantics and Dynamic Occupancy Grid Maps
Rabbia Asghar, Wenqian Liu, Lukas Rummelhard +2
Accurate prediction of driving scenes is essential for road safety and autonomous driving. Occupancy Grid Maps (OGMs) are commonly employed for scene prediction due to their struct…
TLCFuse: Temporal Multi-Modality Fusion Towards Occlusion-Aware Semantic Segmentation-Aided Motion Planning
Gustavo Salazar-Gomez, Wenqian Liu, Manuel Diaz-Zapata +2
In autonomous driving, addressing occlusion scenarios is crucial yet challenging. Robust surrounding perception is essential for handling occlusions and aiding motion planning. Sta…