StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation

TL;DR

StreamingFlow introduces a novel neural ODE-based framework for asynchronous multi-sensor occupancy forecasting in autonomous driving, enabling accurate future environment prediction without strict sensor synchronization.

Contribution

It is the first to integrate neural ODEs into BEV occupancy prediction, allowing asynchronous multi-sensor data fusion and streaming future occupancy forecasting.

Findings

Outperforms existing vision and LiDAR-based methods.

Shows strong zero-shot generalization and future prediction capabilities.

Effective on large-scale datasets nuScenes and Lyft L5.

Abstract

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.