Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks
This work addresses the challenge of long-term, accurate environment forecasting for autonomous vehicle navigation, though it appears incremental by modifying the grid representation rather than introducing a new paradigm.
The paper tackles the problem of predicting dynamic urban traffic scenes for autonomous vehicles by proposing an allo-centric occupancy grid representation, which significantly improves scene prediction compared to conventional ego-centric grids, as validated on the Nuscenes dataset.
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 between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. The results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach.