LOPR: Latent Occupancy PRediction using Generative Models
- URL: http://arxiv.org/abs/2210.01249v3
- Date: Thu, 24 Aug 2023 17:30:57 GMT
- Title: LOPR: Latent Occupancy PRediction using Generative Models
- Authors: Bernard Lange, Masha Itkina, Mykel J. Kochenderfer
- Abstract summary: LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation.
We propose a framework that decouples occupancy prediction into: representation learning and prediction within the learned latent space.
- Score: 49.15687400958916
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Environment prediction frameworks are integral for autonomous vehicles,
enabling safe navigation in dynamic environments. LiDAR generated occupancy
grid maps (L-OGMs) offer a robust bird's eye-view scene representation that
facilitates joint scene predictions without relying on manual labeling unlike
commonly used trajectory prediction frameworks. Prior approaches have optimized
deterministic L-OGM prediction architectures directly in grid cell space. While
these methods have achieved some degree of success in prediction, they
occasionally grapple with unrealistic and incorrect predictions. We claim that
the quality and realism of the forecasted occupancy grids can be enhanced with
the use of generative models. We propose a framework that decouples occupancy
prediction into: representation learning and stochastic prediction within the
learned latent space. Our approach allows for conditioning the model on other
available sensor modalities such as RGB-cameras and high definition maps. We
demonstrate that our approach achieves state-of-the-art performance and is
readily transferable between different robotic platforms on the real-world
NuScenes, Waymo Open, and a custom dataset we collected on an experimental
vehicle platform.
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