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Found 4 papers, 1 papers with code
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
Instead, we shift the paradigm to have the planner query occupancy at relevant spatio-temporal points, restricting the computation to those regions of interest.
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants.
Learning to Search in Task and Motion Planning with Streams
Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables.
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.
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