  A critical task for developing safe autonomous driving stacks is to determine
whether an obstacle is safety-critical, i.e., poses an imminent threat to the
autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability
theory can be applied to compute interaction-dynamics-aware perception safety
zones that better inform an ego vehicle's perception module which obstacles are
considered safety-critical. For completeness, these zones are typically larger
than absolutely necessary, forcing the perception module to pay attention to a
larger collection of objects for the sake of conservatism. As an improvement,
we propose a maneuver-based decomposition of our safety zones that leverages
information about the ego maneuver to reduce the zone volume. In particular, we
propose a "temporal convolution" operation that produces safety zones for
specific ego maneuvers, thus limiting the ego's behavior to reduce the size of
the safety zones. We show with numerical experiments that maneuver-based zones
are significantly smaller (up to 76% size reduction) than the baseline while
maintaining completeness.
