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Vision-centric joint perception and prediction (PnP) has become an emergingtrend in autonomous driving research. It predicts the future states of thetraffic participants in the surrounding environment from raw RGB images.However, it is still a critical challenge to synchronize features obtained atmultiple camera views and timestamps due to inevitable geometric distortionsand further exploit those spatial-temporal features. To address this issue, wepropose a temporal bird's-eye-view pyramid transformer (TBP-Former) forvision-centric PnP, which includes two novel designs. First, apose-synchronized BEV encoder is proposed to map raw image inputs with anycamera pose at any time to a shared and synchronized BEV space for betterspatial-temporal synchronization. Second, a spatial-temporal pyramidtransformer is introduced to comprehensively extract multi-scale BEV featuresand predict future BEV states with the support of spatial-temporal priors.Extensive experiments on nuScenes dataset show that our proposed frameworkoverall outperforms all state-of-the-art vision-based prediction methods.

TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving