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In self-driving, predicting future in terms of location and motion of all theagents around the vehicle is a crucial requirement for planning. Recently, anew joint formulation of perception and prediction has emerged by fusing richsensory information perceived from multiple cameras into a compact bird's-eyeview representation to perform prediction. However, the quality of futurepredictions degrades over time while extending to longer time horizons due tomultiple plausible predictions. In this work, we address this inherentuncertainty in future predictions with a stochastic temporal model. Our modellearns temporal dynamics in a latent space through stochastic residual updatesat each time step. By sampling from a learned distribution at each time step,we obtain more diverse future predictions that are also more accurate comparedto previous work, especially stretching both spatially further regions in thescene and temporally over longer time horizons. Despite separate processing ofeach time step, our model is still efficient through decoupling of the learningof dynamics and the generation of future predictions.

StretchBEV: Stretching Future Instance Prediction Spatially and Temporally