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Over the last few years, we have witnessed tremendous progress on manysubtasks of autonomous driving, including perception, motion forecasting, andmotion planning. However, these systems often assume that the car is accuratelylocalized against a high-definition map. In this paper we question thisassumption, and investigate the issues that arise in state-of-the-art autonomystacks under localization error. Based on our observations, we design a systemthat jointly performs perception, prediction, and localization. Ourarchitecture is able to reuse computation between both tasks, and is thus ableto correct localization errors efficiently. We show experiments on alarge-scale autonomy dataset, demonstrating the efficiency and accuracy of ourproposed approach.

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction