  Autonomous off-road driving requires understanding traversability, which
refers to the suitability of a given terrain to drive over. When offroad
vehicles travel at high speed ($>10m/s$), they need to reason at long-range
($50m$-$100m$) for safe and deliberate navigation. Moreover, vehicles often
operate in new environments and under different weather conditions. LiDAR
provides accurate estimates robust to visual appearances, however, it is often
too noisy beyond 30m for fine-grained estimates due to sparse measurements.
Conversely, visual-based models give dense predictions at further distances but
perform poorly at all ranges when out of training distribution. To address
these challenges, we present ALTER, an offroad perception module that
adapts-on-the-drive to combine the best of both sensors. Our visual model
continuously learns from new near-range LiDAR measurements. This
self-supervised approach enables accurate long-range traversability prediction
in novel environments without hand-labeling. Results on two distinct real-world
offroad environments show up to 52.5% improvement in traversability estimation
over LiDAR-only estimates and 38.1% improvement over non-adaptive visual
baseline.
