280 citations · 349 across the 4 of their papers we have counts for
10 papers · 1 filter
Model-Based Imitation Learning for Urban Driving
Anthony Hu, Gianluca Corrado, Nicolas Griffiths +6
An accurate model of the environment and the dynamic agents acting in it offers great potential for improving motion planning. We present MILE: a Model-based Imitation LEarning app…
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects. Motivate…
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
Mennatullah Siam, Alex Kendall, Martin Jagersand
Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical…
Probabilistic Future Prediction for Video Scene Understanding
Anthony Hu, Fergal Cotter, Nikhil Mohan +2
We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scene…
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
Anthony Hu, Alex Kendall, Roberto Cipolla
We present a novel embedding approach for video instance segmentation. Our method learns a spatio-temporal embedding integrating cues from appearance, motion, and geometry; a 3D ca…
Urban Driving with Conditional Imitation Learning
Jeffrey Hawke, Richard Shen, Corina Gurau +8
Hand-crafting generalised decision-making rules for real-world urban autonomous driving is hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations…