CATPlan: Loss-based Collision Prediction in End-to-End Autonomous Driving
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
In recent years, there has been increased interest in the design, training,
and evaluation of end-to-end autonomous driving (AD) systems. One often
overlooked aspect is the uncertainty of planned trajectories predicted by these
systems, despite awareness of their own uncertainty being key to achieve safety
and robustness. We propose to estimate this uncertainty by adapting loss
prediction from the uncertainty quantification literature. To this end, we
introduce a novel light-weight module, dubbed CATPlan, that is trained to
decode motion and planning embeddings into estimates of the collision loss used
to partially supervise end-to-end AD systems. During inference, these estimates
are interpreted as collision risk. We evaluate CATPlan on the safety-critical,
nerf-based, closed-loop benchmark NeuroNCAP and find that it manages to detect
collisions with a $54.8\%$ relative improvement to average precision over a
GMM-based baseline in which the predicted trajectory is compared to the
forecasted trajectories of other road users. Our findings indicate that the
addition of CATPlan can lead to safer end-to-end AD systems and hope that our
work will spark increased interest in uncertainty quantification for such
systems.
