1 citations · 1 across the 5 of their papers we have counts for
6 papers
Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps
Rabbia Asghar, Manuel Diaz-Zapata, Lukas Rummelhard +2
Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In th…
LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction
Manuel Alejandro Diaz-Zapata, David Sierra González, Özgür Erkent +2
Semantic grids can be useful representations of the scene around an autonomous system. By having information about the layout of the space around itself, a robot can leverage this…
Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks
Rabbia Asghar, Lukas Rummelhard, Anne Spalanzani +1
Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions…
LAPTNet: LiDAR-Aided Perspective Transform Network
Manuel Alejandro Diaz-Zapata, Özgür Erkent, Christian Laugier +2
Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vit…
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning
Khushdeep Singh Mann, Abhishek Tomy, Anshul Paigwar +2
Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation. Common challenges in the prediction include fore…
Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features
Andrés Gómez, Thomas Genevois, Jerome Lussereau +1
Object detection is a critical problem for the safe interaction between autonomous vehicles and road users. Deep-learning methodologies allowed the development of object detection…