  Safety guarantees in motion planning for autonomous driving typically involve
certifying the trajectory to be collision-free under any motion of the
uncontrollable participants in the environment, such as the human-driven
vehicles on the road. As a result they usually employ a conservative bound on
the behavior of such participants, such as reachability analysis. We point out
that planning trajectories to rigorously avoid the entirety of the reachable
regions is unnecessary and too restrictive, because observing the environment
in the future will allow us to prune away most of them; disregarding this
ability to react to future updates could prohibit solutions to scenarios that
are easily navigated by human drivers. We propose to account for the autonomous
vehicle's reactions to future environment changes by a novel safety framework,
Comprehensive Reactive Safety. Validated in simulations in several urban
driving scenarios such as unprotected left turns and lane merging, the
resulting planning algorithm called Reactive ILQR demonstrates strong
negotiation capabilities and better safety at the same time.
