  Self-driving cars must detect other vehicles and pedestrians in 3D to plan
safe routes and avoid collisions. State-of-the-art 3D object detectors, based
on deep learning, have shown promising accuracy but are prone to over-fit to
domain idiosyncrasies, making them fail in new environments -- a serious
problem if autonomous vehicles are meant to operate freely. In this paper, we
propose a novel learning approach that drastically reduces this gap by
fine-tuning the detector on pseudo-labels in the target domain, which our
method generates while the vehicle is parked, based on replays of previously
recorded driving sequences. In these replays, objects are tracked over time,
and detections are interpolated and extrapolated -- crucially, leveraging
future information to catch hard cases. We show, on five autonomous driving
datasets, that fine-tuning the object detector on these pseudo-labels
substantially reduces the domain gap to new driving environments, yielding
drastic improvements in accuracy and detection reliability.
