Occupancy Prediction with Temporal Transformers for Driving

摘要

We present a temporal transformer approach for 3D occupancy prediction in autonomous driving. Our method predicts future 3D occupancy states by modeling temporal dependencies across multiple frames, enabling accurate forecasting of dynamic obstacles and free space for safe navigation.