  Predicting the future occupancy states of the surrounding environment is a
vital task for autonomous driving. However, current best-performing
single-modality methods or multi-modality fusion perception methods are only
able to predict uniform snapshots of future occupancy states and require
strictly synchronized sensory data for sensor fusion. We propose a novel
framework, StreamingFlow, to lift these strong limitations. StreamingFlow is a
novel BEV occupancy predictor that ingests asynchronous multi-sensor data
streams for fusion and performs streaming forecasting of the future occupancy
map at any future timestamps. By integrating neural ordinary differential
equations (N-ODE) into recurrent neural networks, StreamingFlow learns
derivatives of BEV features over temporal horizons, updates the implicit
sensor's BEV features as part of the fusion process, and propagates BEV states
to the desired future time point. It shows good zero-shot generalization
ability of prediction, reflected in the interpolation of the observed
prediction time horizon and the reasonable inference of the unseen farther
future period. Extensive experiments on two large-scale datasets, nuScenes and
Lyft L5, demonstrate that StreamingFlow significantly outperforms previous
vision-based, LiDAR-based methods, and shows superior performance compared to
state-of-the-art fusion-based methods.
